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International Conference on Computational Linguistics 1

https://aclanthology.org/2024.lrec-main.301.bib

https://aclanthology.org/2024.lrec-main.301/

@inproceedings{yuan-etal-2024-co3, title = "{CO}3: Low-resource Contrastive Co-training for Generative Conversational Query Rewrite", author = "Yuan, Yifei and Shi, Chen and Runze, Wang and Chen, Liyi and Hu, Renjun and Zhang, Zengming and Jiang, Feijun and Lam, Wai", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.301", pages = "3394--3406", abstract = "Generative query rewrite generates reconstructed query rewrites using the conversation history while rely heavily on gold rewrite pairs that are expensive to obtain. Recently, few-shot learning is gaining increasing popularity for this task, whereas these methods are sensitive to the inherent noise due to limited data size. Besides, both attempts face performance degradation when there exists language style shift between training and testing cases. To this end, we study low-resource generative conversational query rewrite that is robust to both noise and language style shift. The core idea is to utilize massive unlabeled data to make further improvements via a contrastive co-training paradigm. Specifically, we co-train two dual models (namely Rewriter and Simplifier) such that each of them provides extra guidance through pseudo-labeling for enhancing the other in an iterative manner. We also leverage contrastive learning with data augmentation, which enables our model pay more attention on the truly valuable information than the noise. Extensive experiments demonstrate the superiority of our model under both few-shot and zero-shot scenarios. We also verify the better generalization ability of our model when encountering language style shift.", }

Generative query rewrite generates reconstructed query rewrites using the conversation history while rely heavily on gold rewrite pairs that are expensive to obtain. Recently, few-shot learning is gaining increasing popularity for this task, whereas these methods are sensitive to the inherent noise due to limited data size. Besides, both attempts face performance degradation when there exists language style shift between training and testing cases. To this end, we study low-resource generative conversational query rewrite that is robust to both noise and language style shift. The core idea is to utilize massive unlabeled data to make further improvements via a contrastive co-training paradigm. Specifically, we co-train two dual models (namely Rewriter and Simplifier) such that each of them provides extra guidance through pseudo-labeling for enhancing the other in an iterative manner. We also leverage contrastive learning with data augmentation, which enables our model pay more attention on the truly valuable information than the noise. Extensive experiments demonstrate the superiority of our model under both few-shot and zero-shot scenarios. We also verify the better generalization ability of our model when encountering language style shift.

CO3: Low-resource Contrastive Co-training for Generative Conversational Query Rewrite

lrec-main.301

Poster

2403.11873

https://aclanthology.org/2024.lrec-main.302.bib

https://aclanthology.org/2024.lrec-main.302/

@inproceedings{coats-2024-coanzse, title = "{C}o{ANZSE} Audio: Creation of an Online Corpus for Linguistic and Phonetic Analysis of {A}ustralian and {N}ew {Z}ealand Englishes", author = "Coats, Steven", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.302", pages = "3407--3412", abstract = "CoANZSE Audio is a searchable online version of the Corpus of Australian and New Zealand Spoken English, a 195-million-word collection of geo-located YouTube transcripts of local government channels. In addition to the part-of-speech-tagged and lemmatized transcript data, CoANZSE Audio provides access to almost all of the underlying audio, as well as to forced alignments of the audio with transcript content, in Praat{'}s TextGrid format. This paper describes the methods used to create the corpus from open-source tools and the architecture of the CoANZSE Audio website. Two possible linguistic analyses based on CoANZSE Audio data are described: use of double modals, a rare syntactic feature, and raising of the mid front vowel /ɛ/ in New Zealand English. CoANZSE Audio can be considered to be among the first large, free, fully searchable online corpora containing data suitable for acoustic phonetic analyses in addition to lexical, grammatical, and discourse properties of Australian and New Zealand Englishes.", }

CoANZSE Audio is a searchable online version of the Corpus of Australian and New Zealand Spoken English, a 195-million-word collection of geo-located YouTube transcripts of local government channels. In addition to the part-of-speech-tagged and lemmatized transcript data, CoANZSE Audio provides access to almost all of the underlying audio, as well as to forced alignments of the audio with transcript content, in Praat{'}s TextGrid format. This paper describes the methods used to create the corpus from open-source tools and the architecture of the CoANZSE Audio website. Two possible linguistic analyses based on CoANZSE Audio data are described: use of double modals, a rare syntactic feature, and raising of the mid front vowel /ɛ/ in New Zealand English. CoANZSE Audio can be considered to be among the first large, free, fully searchable online corpora containing data suitable for acoustic phonetic analyses in addition to lexical, grammatical, and discourse properties of Australian and New Zealand Englishes.

CoANZSE Audio: Creation of an Online Corpus for Linguistic and Phonetic Analysis of Australian and New Zealand Englishes

lrec-main.302

Poster

https://aclanthology.org/2024.lrec-main.303.bib

https://aclanthology.org/2024.lrec-main.303/

@inproceedings{keyaki-keyaki-2024-coarse, title = "Coarse-Tuning for Ad-hoc Document Retrieval Using Pre-trained Language Models", author = "Keyaki, Atsushi and Keyaki, Ribeka", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.303", pages = "3413--3421", abstract = "Fine-tuning in information retrieval systems using pre-trained language models (PLM-based IR) requires learning query representations and query-document relations, in addition to downstream task-specific learning. This study introduces coarse-tuning as an intermediate learning stage that bridges pre-training and fine-tuning. By learning query representations and query-document relations in coarse-tuning, we aim to reduce the load of fine-tuning and improve the learning effect of downstream IR tasks. We propose Query-Document Pair Prediction (QDPP) for coarse-tuning, which predicts the appropriateness of query-document pairs. Evaluation experiments show that the proposed method significantly improves MRR and/or nDCG@5 in four ad-hoc document retrieval datasets. Furthermore, the results of the query prediction task suggested that coarse-tuning facilitated learning of query representation and query-document relations.", }

Fine-tuning in information retrieval systems using pre-trained language models (PLM-based IR) requires learning query representations and query-document relations, in addition to downstream task-specific learning. This study introduces coarse-tuning as an intermediate learning stage that bridges pre-training and fine-tuning. By learning query representations and query-document relations in coarse-tuning, we aim to reduce the load of fine-tuning and improve the learning effect of downstream IR tasks. We propose Query-Document Pair Prediction (QDPP) for coarse-tuning, which predicts the appropriateness of query-document pairs. Evaluation experiments show that the proposed method significantly improves MRR and/or nDCG@5 in four ad-hoc document retrieval datasets. Furthermore, the results of the query prediction task suggested that coarse-tuning facilitated learning of query representation and query-document relations.

Coarse-Tuning for Ad-hoc Document Retrieval Using Pre-trained Language Models

lrec-main.303

Poster

2403.16915

https://aclanthology.org/2024.lrec-main.304.bib

https://aclanthology.org/2024.lrec-main.304/

@inproceedings{petrova-etal-2024-cobald, title = "{C}o{B}a{LD} Annotation: The Enrichment of the Enhanced {U}niversal {D}ependencies with the Semantical Pattern", author = "Petrova, Maria Andreevna and Ivoylova, Alexandra M. and Tishchenkova, Anastasia", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.304", pages = "3422--3432", abstract = "The paper is devoted to the annotation format aimed at morphological, syntactic and especially semantic markup. The format combines the Enhanced UD morphosyntax and the Compreno semantic pattern, enriching the UD annotation with word meanings and labels for semantic relations between words. To adapt the Compreno semantics for the current purpose, we reduced the number of the semantic fields denoting lexical meanings by using hyperonym fields. Moreover, we used a generalized variant of the semantic relations as the original roles possess rather narrow meanings which makes them too numerous. Creating such a format demands the Compreno-to-UD morphosyntax conversion as well, which, in turn, demands solving the asymmetry problem between the models. The asymmetry concerns tokenization, lemmatization, POS-tagging, sets of grammatical features and dependency heads. To overcome this problem, the Compreno-to-UD converter was created. As an application, the work presents a 150,000 token corpus of English news annotated according to the standard.", }

The paper is devoted to the annotation format aimed at morphological, syntactic and especially semantic markup. The format combines the Enhanced UD morphosyntax and the Compreno semantic pattern, enriching the UD annotation with word meanings and labels for semantic relations between words. To adapt the Compreno semantics for the current purpose, we reduced the number of the semantic fields denoting lexical meanings by using hyperonym fields. Moreover, we used a generalized variant of the semantic relations as the original roles possess rather narrow meanings which makes them too numerous. Creating such a format demands the Compreno-to-UD morphosyntax conversion as well, which, in turn, demands solving the asymmetry problem between the models. The asymmetry concerns tokenization, lemmatization, POS-tagging, sets of grammatical features and dependency heads. To overcome this problem, the Compreno-to-UD converter was created. As an application, the work presents a 150,000 token corpus of English news annotated according to the standard.

CoBaLD Annotation: The Enrichment of the Enhanced Universal Dependencies with the Semantical Pattern

lrec-main.304

Poster

https://aclanthology.org/2024.lrec-main.305.bib

https://aclanthology.org/2024.lrec-main.305/

@inproceedings{ding-etal-2024-cocomic, title = "{C}o{C}o{MIC}: Code Completion by Jointly Modeling In-file and Cross-file Context", author = "Ding, Yangruibo and Wang, Zijian and Ahmad, Wasi and Ramanathan, Murali Krishna and Nallapati, Ramesh and Bhatia, Parminder and Roth, Dan and Xiang, Bing", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.305", pages = "3433--3445", abstract = "While pre-trained language models (LM) for code have achieved great success in code completion, they generate code conditioned only on the contents within the file, i.e., in-file context, but ignore the rich semantics in other files within the same project, i.e., project-level cross-file context, a critical source of information that is especially useful in modern modular software development. Such overlooking constrains code LMs{'} capacity in code completion, leading to unexpected behaviors such as generating hallucinated class member functions or function calls with unexpected arguments. In this work, we propose CoCoMIC, a novel framework that jointly learns the in-file and cross-file context on top of code LMs. To empower CoCoMIC, we develop CCFinder, a static-analysis-based tool that locates and retrieves the most relevant project-level cross-file context for code completion. CoCoMIC successfully improves the existing code LM with a 33.94{\%} relative increase in exact match and 28.69{\%} in identifier matching for code completion when the cross-file context is provided. Finally, we perform a series of ablation studies and share valuable insights for future research on integrating cross-file context into code LMs.", }

While pre-trained language models (LM) for code have achieved great success in code completion, they generate code conditioned only on the contents within the file, i.e., in-file context, but ignore the rich semantics in other files within the same project, i.e., project-level cross-file context, a critical source of information that is especially useful in modern modular software development. Such overlooking constrains code LMs{'} capacity in code completion, leading to unexpected behaviors such as generating hallucinated class member functions or function calls with unexpected arguments. In this work, we propose CoCoMIC, a novel framework that jointly learns the in-file and cross-file context on top of code LMs. To empower CoCoMIC, we develop CCFinder, a static-analysis-based tool that locates and retrieves the most relevant project-level cross-file context for code completion. CoCoMIC successfully improves the existing code LM with a 33.94{\%} relative increase in exact match and 28.69{\%} in identifier matching for code completion when the cross-file context is provided. Finally, we perform a series of ablation studies and share valuable insights for future research on integrating cross-file context into code LMs.

CoCoMIC: Code Completion by Jointly Modeling In-file and Cross-file Context

lrec-main.305

Poster

2212.10007

https://aclanthology.org/2024.lrec-main.306.bib

https://aclanthology.org/2024.lrec-main.306/

@inproceedings{an-etal-2024-code, title = "Code Defect Detection Using Pre-trained Language Models with Encoder-Decoder via Line-Level Defect Localization", author = "An, Jimin and Choi, YunSeok and Lee, Jee-Hyong", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.306", pages = "3446--3456", abstract = "Recently, code Pre-trained Language Models (PLMs) trained on large amounts of code and comment, have shown great success in code defect detection tasks. However, most PLMs simply treated the code as a single sequence and only used the encoder of PLMs to determine if there exist defects in the entire code. For a more analyzable and explainable approach, it is crucial to identify which lines contain defects. In this paper, we propose a novel method for code defect detection that integrates line-level defect localization into a unified training process. To identify code defects at the line-level, we convert the code into a sequence separated by lines using a special token. Then, to utilize the characteristic that both the encoder and decoder of PLMs process information differently, we leverage both the encoder and decoder for line-level defect localization. By learning code defect detection and line-level defect localization tasks in a unified manner, our proposed method promotes knowledge sharing between the two tasks. We demonstrate that our proposed method significantly improves performance on four benchmark datasets for code defect detection. Additionally, we show that our method can be easily integrated with ChatGPT.", }

Recently, code Pre-trained Language Models (PLMs) trained on large amounts of code and comment, have shown great success in code defect detection tasks. However, most PLMs simply treated the code as a single sequence and only used the encoder of PLMs to determine if there exist defects in the entire code. For a more analyzable and explainable approach, it is crucial to identify which lines contain defects. In this paper, we propose a novel method for code defect detection that integrates line-level defect localization into a unified training process. To identify code defects at the line-level, we convert the code into a sequence separated by lines using a special token. Then, to utilize the characteristic that both the encoder and decoder of PLMs process information differently, we leverage both the encoder and decoder for line-level defect localization. By learning code defect detection and line-level defect localization tasks in a unified manner, our proposed method promotes knowledge sharing between the two tasks. We demonstrate that our proposed method significantly improves performance on four benchmark datasets for code defect detection. Additionally, we show that our method can be easily integrated with ChatGPT.

Code Defect Detection Using Pre-trained Language Models with Encoder-Decoder via Line-Level Defect Localization

lrec-main.306

Poster

https://aclanthology.org/2024.lrec-main.307.bib

https://aclanthology.org/2024.lrec-main.307/

@inproceedings{laureano-de-leon-etal-2024-code, title = "Code-Mixed Probes Show How Pre-Trained Models Generalise on Code-Switched Text", author = "Laureano De Leon, Frances Adriana and Tayyar Madabushi, Harish and Lee, Mark", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.307", pages = "3457--3468", abstract = "Code-switching is a prevalent linguistic phenomenon in which multilingual individuals seamlessly alternate between languages. Despite its widespread use online and recent research trends in this area, research in code-switching presents unique challenges, primarily stemming from the scarcity of labelled data and available resources. In this study we investigate how pre-trained Language Models handle code-switched text in three dimensions: a) the ability of PLMs to detect code-switched text, b) variations in the structural information that PLMs utilise to capture code-switched text, and c) the consistency of semantic information representation in code-switched text. To conduct a systematic and controlled evaluation of the language models in question, we create a novel dataset of well-formed naturalistic code-switched text along with parallel translations into the source languages. Our findings reveal that pre-trained language models are effective in generalising to code-switched text, shedding light on abilities of these models to generalise representations to CS corpora. We release all our code and data, including the novel corpus, at https://github.com/francesita/code-mixed-probes.", }

Code-switching is a prevalent linguistic phenomenon in which multilingual individuals seamlessly alternate between languages. Despite its widespread use online and recent research trends in this area, research in code-switching presents unique challenges, primarily stemming from the scarcity of labelled data and available resources. In this study we investigate how pre-trained Language Models handle code-switched text in three dimensions: a) the ability of PLMs to detect code-switched text, b) variations in the structural information that PLMs utilise to capture code-switched text, and c) the consistency of semantic information representation in code-switched text. To conduct a systematic and controlled evaluation of the language models in question, we create a novel dataset of well-formed naturalistic code-switched text along with parallel translations into the source languages. Our findings reveal that pre-trained language models are effective in generalising to code-switched text, shedding light on abilities of these models to generalise representations to CS corpora. We release all our code and data, including the novel corpus, at https://github.com/francesita/code-mixed-probes.

Code-Mixed Probes Show How Pre-Trained Models Generalise on Code-Switched Text

lrec-main.307

Poster

2403.04872

https://aclanthology.org/2024.lrec-main.308.bib

https://aclanthology.org/2024.lrec-main.308/

@inproceedings{kronis-etal-2024-code, title = "Code-Mixed Text Augmentation for {L}atvian {ASR}", author = "Kronis, Martins and Salimbajevs, Askars and Pinnis, M{\=a}rcis", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.308", pages = "3469--3479", abstract = "Code-mixing has become mainstream in the modern, globalised world and affects low-resource languages, such as Latvian, in particular. Solutions to developing an automatic speech recognition system (ASR) for code-mixed speech often rely on specially created audio-text corpora, which are expensive and time-consuming to create. In this work, we attempt to tackle code-mixed Latvian-English speech recognition by improving the language model (LM) of a hybrid ASR system. We make a distinction between inflected transliterations and phonetic transcriptions as two different foreign word types. We propose an inflected transliteration model and a phonetic transcription model for the automatic generation of said word types. We then leverage a large human-translated English-Latvian parallel text corpus to generate synthetic code-mixed Latvian sentences by substituting in generated foreign words. Using the newly created augmented corpora, we train a new LM and combine it with our existing Latvian acoustic model (AM). For evaluation, we create a specialised foreign word test set on which our methods yield up to 15{\%} relative CER improvement. We then further validate these results in a human evaluation campaign.", }

Code-mixing has become mainstream in the modern, globalised world and affects low-resource languages, such as Latvian, in particular. Solutions to developing an automatic speech recognition system (ASR) for code-mixed speech often rely on specially created audio-text corpora, which are expensive and time-consuming to create. In this work, we attempt to tackle code-mixed Latvian-English speech recognition by improving the language model (LM) of a hybrid ASR system. We make a distinction between inflected transliterations and phonetic transcriptions as two different foreign word types. We propose an inflected transliteration model and a phonetic transcription model for the automatic generation of said word types. We then leverage a large human-translated English-Latvian parallel text corpus to generate synthetic code-mixed Latvian sentences by substituting in generated foreign words. Using the newly created augmented corpora, we train a new LM and combine it with our existing Latvian acoustic model (AM). For evaluation, we create a specialised foreign word test set on which our methods yield up to 15{\%} relative CER improvement. We then further validate these results in a human evaluation campaign.

Code-Mixed Text Augmentation for Latvian ASR

lrec-main.308

Poster

https://aclanthology.org/2024.lrec-main.309.bib

https://aclanthology.org/2024.lrec-main.309/

@inproceedings{harada-oseki-2024-cognitive, title = "Cognitive Information Bottleneck: Extracting Minimal Sufficient Cognitive Language Processing Signals", author = "Harada, Yuto and Oseki, Yohei", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.309", pages = "3480--3489", abstract = "In Reinforcement Learning from Human Feedback (RLHF), explicit human feedback, such as rankings, is employed to align Natural Language Processing (NLP) models with human preferences. In contrast, the potential of implicit human feedback, encompassing cognitive processing signals like eye-tracking and brain activity, remains underexplored. These signals capture unconscious human responses but are often marred by noise and redundancy, complicating their application to specific tasks. To address this issue, we introduce the Cognitive Information Bottleneck (CIB), a method that extracts only the task-relevant information from cognitive processing signals. Grounded in the principles of the information bottleneck, CIB aims to learn representations that maximize the mutual information between the representations and targets while minimizing the mutual information between inputs and representations. By employing CIB to filter out redundant information from cognitive processing signals, our goal is to provide representations that are both minimal and sufficient. This approach enables more efficient fitting of models to inputs. Our results show that the proposed method outperforms existing methods in efficiently compressing various cognitive processing signals and significantly enhances performance on downstream tasks. Evaluated on public datasets, our model surpasses contemporary state-of-the-art models. Furthermore, by analyzing these compressed representations, we offer insights into how cognitive processing signals can be leveraged to improve performance.", }

In Reinforcement Learning from Human Feedback (RLHF), explicit human feedback, such as rankings, is employed to align Natural Language Processing (NLP) models with human preferences. In contrast, the potential of implicit human feedback, encompassing cognitive processing signals like eye-tracking and brain activity, remains underexplored. These signals capture unconscious human responses but are often marred by noise and redundancy, complicating their application to specific tasks. To address this issue, we introduce the Cognitive Information Bottleneck (CIB), a method that extracts only the task-relevant information from cognitive processing signals. Grounded in the principles of the information bottleneck, CIB aims to learn representations that maximize the mutual information between the representations and targets while minimizing the mutual information between inputs and representations. By employing CIB to filter out redundant information from cognitive processing signals, our goal is to provide representations that are both minimal and sufficient. This approach enables more efficient fitting of models to inputs. Our results show that the proposed method outperforms existing methods in efficiently compressing various cognitive processing signals and significantly enhances performance on downstream tasks. Evaluated on public datasets, our model surpasses contemporary state-of-the-art models. Furthermore, by analyzing these compressed representations, we offer insights into how cognitive processing signals can be leveraged to improve performance.

Cognitive Information Bottleneck: Extracting Minimal Sufficient Cognitive Language Processing Signals

lrec-main.309

Poster

https://aclanthology.org/2024.lrec-main.310.bib

https://aclanthology.org/2024.lrec-main.310/

@inproceedings{wei-etal-2024-collabkg, title = "{C}ollab{KG}: A Learnable Human-Machine-Cooperative Information Extraction Toolkit for (Event) Knowledge Graph Construction", author = "Wei, Xiang and Chen, Yufeng and Cheng, Ning and Cui, Xingyu and Xu, Jinan and Han, Wenjuan", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.310", pages = "3490--3506", abstract = "In order to construct or extend entity-centric and event-centric knowledge graphs (KG and EKG), the information extraction (IE) annotation toolkit is essential. However, existing IE toolkits have several non-trivial problems, such as not supporting multi-tasks, and not supporting automatic updates. In this work, we present CollabKG, a learnable human-machine-cooperative IE toolkit for KG and EKG construction. Specifically, for the multi-task issue, CollabKG unifies different IE subtasks, including named entity recognition (NER), entity-relation triple extraction (RE), and event extraction (EE), and supports both KG and EKG. Then, combining advanced prompting-based IE technology, the human-machine-cooperation mechanism with Large Language Models (LLMs) as the assistant machine is presented which can provide a lower cost as well as a higher performance. Lastly, owing to the two-way interaction between the human and machine, CollabKG with learning ability allows self-renewal. Besides, CollabKG has several appealing features (e.g., customization, training-free, and label propagation) that make the system powerful and high-productivity. We holistically compare our toolkit with other existing tools on these features. Human evaluation quantitatively illustrates that CollabKG significantly improves annotation quality, efficiency, and stability simultaneously.", }

In order to construct or extend entity-centric and event-centric knowledge graphs (KG and EKG), the information extraction (IE) annotation toolkit is essential. However, existing IE toolkits have several non-trivial problems, such as not supporting multi-tasks, and not supporting automatic updates. In this work, we present CollabKG, a learnable human-machine-cooperative IE toolkit for KG and EKG construction. Specifically, for the multi-task issue, CollabKG unifies different IE subtasks, including named entity recognition (NER), entity-relation triple extraction (RE), and event extraction (EE), and supports both KG and EKG. Then, combining advanced prompting-based IE technology, the human-machine-cooperation mechanism with Large Language Models (LLMs) as the assistant machine is presented which can provide a lower cost as well as a higher performance. Lastly, owing to the two-way interaction between the human and machine, CollabKG with learning ability allows self-renewal. Besides, CollabKG has several appealing features (e.g., customization, training-free, and label propagation) that make the system powerful and high-productivity. We holistically compare our toolkit with other existing tools on these features. Human evaluation quantitatively illustrates that CollabKG significantly improves annotation quality, efficiency, and stability simultaneously.

CollabKG: A Learnable Human-Machine-Cooperative Information Extraction Toolkit for (Event) Knowledge Graph Construction

lrec-main.310

Poster

2307.00769

https://aclanthology.org/2024.lrec-main.311.bib

https://aclanthology.org/2024.lrec-main.311/

@inproceedings{zhou-etal-2024-collecting, title = "Collecting and Analyzing Dialogues in a Tagline Co-Writing Task", author = "Zhou, Xulin and Ichikawa, Takuma and Higashinaka, Ryuichiro", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.311", pages = "3507--3517", abstract = "The potential usage scenarios of dialogue systems will be greatly expanded if they are able to collaborate more creatively with humans. Many studies have examined ways of building such systems, but most of them focus on problem-solving dialogues, and relatively little research has been done on systems that can engage in creative collaboration with users. In this study, we designed a tagline co-writing task in which two people collaborate to create taglines via text chat, created an interface for data collection, and collected dialogue logs, editing logs, and questionnaire results. In total, we collected 782 Japanese dialogues. We describe the characteristic interactions comprising the tagline co-writing task and report the results of our analysis, in which we examined the kind of utterances that appear in the dialogues as well as the most frequent expressions found in highly rated dialogues in subjective evaluations. We also analyzed the relationship between subjective evaluations and workflow utilized in the dialogues and the interplay between taglines and utterances.", }

The potential usage scenarios of dialogue systems will be greatly expanded if they are able to collaborate more creatively with humans. Many studies have examined ways of building such systems, but most of them focus on problem-solving dialogues, and relatively little research has been done on systems that can engage in creative collaboration with users. In this study, we designed a tagline co-writing task in which two people collaborate to create taglines via text chat, created an interface for data collection, and collected dialogue logs, editing logs, and questionnaire results. In total, we collected 782 Japanese dialogues. We describe the characteristic interactions comprising the tagline co-writing task and report the results of our analysis, in which we examined the kind of utterances that appear in the dialogues as well as the most frequent expressions found in highly rated dialogues in subjective evaluations. We also analyzed the relationship between subjective evaluations and workflow utilized in the dialogues and the interplay between taglines and utterances.

Collecting and Analyzing Dialogues in a Tagline Co-Writing Task

lrec-main.311

Poster

https://aclanthology.org/2024.lrec-main.312.bib

https://aclanthology.org/2024.lrec-main.312/

@inproceedings{katada-etal-2024-collecting, title = "Collecting Human-Agent Dialogue Dataset with Frontal Brain Signal toward Capturing Unexpressed Sentiment", author = "Katada, Shun and Takeda, Ryu and Komatani, Kazunori", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.312", pages = "3518--3528", abstract = "Multimodal information such as text and audiovisual data has been used for emotion/sentiment estimation during human-agent dialogue; however, user sentiments are not necessarily expressed explicitly during dialogues. Biosignals such as brain signals recorded using an electroencephalogram (EEG) sensor have been the subject of focus in affective computing regions to capture unexpressed emotional changes in a controlled experimental environment. In this study, we collect and analyze multimodal data with an EEG during a human-agent dialogue toward capturing unexpressed sentiment. Our contributions are as follows: (1) a new multimodal human-agent dialogue dataset is created, which includes not only text and audiovisual data but also frontal EEGs and physiological signals during the dialogue. In total, about 500-minute chat dialogues were collected from thirty participants aged 20 to 70. (2) We present a novel method for dealing with eye-blink noise for frontal EEGs denoising. This method applies facial landmark tracking to detect and delete eye-blink noise. (3) An experimental evaluation showed the effectiveness of the frontal EEGs. It improved sentiment estimation performance when used with other modalities by multimodal fusion, although it only has three channels.", }

Multimodal information such as text and audiovisual data has been used for emotion/sentiment estimation during human-agent dialogue; however, user sentiments are not necessarily expressed explicitly during dialogues. Biosignals such as brain signals recorded using an electroencephalogram (EEG) sensor have been the subject of focus in affective computing regions to capture unexpressed emotional changes in a controlled experimental environment. In this study, we collect and analyze multimodal data with an EEG during a human-agent dialogue toward capturing unexpressed sentiment. Our contributions are as follows: (1) a new multimodal human-agent dialogue dataset is created, which includes not only text and audiovisual data but also frontal EEGs and physiological signals during the dialogue. In total, about 500-minute chat dialogues were collected from thirty participants aged 20 to 70. (2) We present a novel method for dealing with eye-blink noise for frontal EEGs denoising. This method applies facial landmark tracking to detect and delete eye-blink noise. (3) An experimental evaluation showed the effectiveness of the frontal EEGs. It improved sentiment estimation performance when used with other modalities by multimodal fusion, although it only has three channels.

Collecting Human-Agent Dialogue Dataset with Frontal Brain Signal toward Capturing Unexpressed Sentiment

lrec-main.312

Poster

https://aclanthology.org/2024.lrec-main.313.bib

https://aclanthology.org/2024.lrec-main.313/

@inproceedings{olstad-etal-2024-collecting, title = "Collecting Linguistic Resources for Assessing Children{'}s Pronunciation of {N}ordic Languages", author = {Olstad, Anne Marte Haug and Smolander, Anna and Str{\"o}mbergsson, Sofia and Ylinen, Sari and Lehtonen, Minna and Kurimo, Mikko and Getman, Yaroslav and Gr{\'o}sz, Tam{\'a}s and Cao, Xinwei and Svendsen, Torbj{\o}rn and Salvi, Giampiero}, editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.313", pages = "3529--3537", abstract = "This paper reports on the experience collecting a number of corpora of Nordic languages spoken by children. The aim of the data collection is providing annotated data to develop and evaluate computer assisted pronunciation assessment systems both for non-native children learning a Nordic language (L2) and for L1 children with speech sound disorder (SSD). The paper presents the challenges encountered recording and annotating data for Finnish, Swedish and Norwegian, as well as the ethical considerations related with making this data publicly available. We hope that sharing this experience will encourage others to collect similar data for other languages. Of the different data collections, we were able to make the Norwegian corpus publicly available in the hope that it will serve as a reference in pronunciation assessment research.", }

This paper reports on the experience collecting a number of corpora of Nordic languages spoken by children. The aim of the data collection is providing annotated data to develop and evaluate computer assisted pronunciation assessment systems both for non-native children learning a Nordic language (L2) and for L1 children with speech sound disorder (SSD). The paper presents the challenges encountered recording and annotating data for Finnish, Swedish and Norwegian, as well as the ethical considerations related with making this data publicly available. We hope that sharing this experience will encourage others to collect similar data for other languages. Of the different data collections, we were able to make the Norwegian corpus publicly available in the hope that it will serve as a reference in pronunciation assessment research.

Collecting Linguistic Resources for Assessing Children's Pronunciation of Nordic Languages

lrec-main.313

Poster

https://aclanthology.org/2024.lrec-main.314.bib

https://aclanthology.org/2024.lrec-main.314/

@inproceedings{atwell-etal-2024-combining, title = "Combining Discourse Coherence with Large Language Models for More Inclusive, Equitable, and Robust Task-Oriented Dialogue", author = "Atwell, Katherine and Inan, Mert and Sicilia, Anthony B. and Alikhani, Malihe", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.314", pages = "3538--3552", abstract = "Large language models (LLMs) are capable of generating well-formed responses, but using LLMs to generate responses on the fly is not yet feasible for many task-oriented systems. Modular architectures are often still required for safety and privacy guarantees on the output. We hypothesize that an offline generation approach using discourse theories, formal grammar rules, and LLMs can allow us to generate human-like, coherent text in a more efficient, robust, and inclusive manner within a task-oriented setting. To this end, we present the first discourse-aware multimodal task-oriented dialogue system that combines discourse theories with offline LLM generation. We deploy our bot as an app to the general public and keep track of the user ratings for six months. Our user ratings show an improvement from 2.8 to 3.5 out of 5 with the introduction of discourse coherence theories. We also show that our model reduces misunderstandings in the dialect of African-American Vernacular English from 93{\%} to 57{\%}. While terms of use prevent us from releasing our entire codebase, we release our code in a format that can be integrated into most existing dialogue systems.", }

Large language models (LLMs) are capable of generating well-formed responses, but using LLMs to generate responses on the fly is not yet feasible for many task-oriented systems. Modular architectures are often still required for safety and privacy guarantees on the output. We hypothesize that an offline generation approach using discourse theories, formal grammar rules, and LLMs can allow us to generate human-like, coherent text in a more efficient, robust, and inclusive manner within a task-oriented setting. To this end, we present the first discourse-aware multimodal task-oriented dialogue system that combines discourse theories with offline LLM generation. We deploy our bot as an app to the general public and keep track of the user ratings for six months. Our user ratings show an improvement from 2.8 to 3.5 out of 5 with the introduction of discourse coherence theories. We also show that our model reduces misunderstandings in the dialect of African-American Vernacular English from 93{\%} to 57{\%}. While terms of use prevent us from releasing our entire codebase, we release our code in a format that can be integrated into most existing dialogue systems.

Combining Discourse Coherence with Large Language Models for More Inclusive, Equitable, and Robust Task-Oriented Dialogue

lrec-main.314

Poster

https://aclanthology.org/2024.lrec-main.315.bib

https://aclanthology.org/2024.lrec-main.315/

@inproceedings{falcao-etal-2024-comet, title = "{COMET} for Low-Resource Machine Translation Evaluation: A Case Study of {E}nglish-{M}altese and {S}panish-{B}asque", author = "Falc{\~a}o, J{\'u}lia and Borg, Claudia and Aranberri, Nora and Abela, Kurt", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.315", pages = "3553--3565", abstract = "Trainable metrics for machine translation evaluation have been scoring the highest correlations with human judgements in the latest meta-evaluations, outperforming traditional lexical overlap metrics such as BLEU, which is still widely used despite its well-known shortcomings. In this work we look at COMET, a prominent neural evaluation system proposed in 2020, to analyze the extent of its language support restrictions, and to investigate strategies to extend this support to new, under-resourced languages. Our case study focuses on English-Maltese and Spanish-Basque. We run a crowd-based evaluation campaign to collect direct assessments and use the annotated dataset to evaluate COMET-22, further fine-tune it, and to train COMET models from scratch for the two language pairs. Our analysis suggests that COMET{'}s performance can be improved with fine-tuning, and that COMET can be highly susceptible to the distribution of scores in the training data, which especially impacts low-resource scenarios.", }

Trainable metrics for machine translation evaluation have been scoring the highest correlations with human judgements in the latest meta-evaluations, outperforming traditional lexical overlap metrics such as BLEU, which is still widely used despite its well-known shortcomings. In this work we look at COMET, a prominent neural evaluation system proposed in 2020, to analyze the extent of its language support restrictions, and to investigate strategies to extend this support to new, under-resourced languages. Our case study focuses on English-Maltese and Spanish-Basque. We run a crowd-based evaluation campaign to collect direct assessments and use the annotated dataset to evaluate COMET-22, further fine-tune it, and to train COMET models from scratch for the two language pairs. Our analysis suggests that COMET{'}s performance can be improved with fine-tuning, and that COMET can be highly susceptible to the distribution of scores in the training data, which especially impacts low-resource scenarios.

COMET for Low-Resource Machine Translation Evaluation: A Case Study of English-Maltese and Spanish-Basque

lrec-main.315

Poster

https://aclanthology.org/2024.lrec-main.316.bib

https://aclanthology.org/2024.lrec-main.316/

@inproceedings{martinek-etal-2024-comicorda, title = "{COMICORDA}: Dialogue Act Recognition in Comic Books", author = "Martinek, Jiri and Kral, Pavel and Lenc, Ladislav and Baloun, Josef", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.316", pages = "3566--3578", abstract = "Dialogue act (DA) recognition is usually realized from a speech signal that is transcribed and segmented into text. However, only a little work in DA recognition from images exists. Therefore, this paper concentrates on this modality and presents a novel DA recognition approach for image documents, namely comic books. To the best of our knowledge, this is the first study investigating dialogue acts from comic books and represents the first steps to building a model for comic book understanding. The proposed method is composed of the following steps: speech balloon segmentation, optical character recognition (OCR), and DA recognition itself. We use YOLOv8 for balloon segmentation, Google Vision for OCR, and Transformer-based models for DA classification. The experiments are performed on a newly created dataset comprising 1,438 annotated comic panels. It contains bounding boxes, transcriptions, and dialogue act annotation. We have achieved nearly 98{\%} average precision for speech balloon segmentation and exceeded the accuracy of 70{\%} for the DA recognition task. We also present an analysis of dialogue structure in the comics domain and compare it with the standard DA datasets, representing another contribution of this paper.", }

Dialogue act (DA) recognition is usually realized from a speech signal that is transcribed and segmented into text. However, only a little work in DA recognition from images exists. Therefore, this paper concentrates on this modality and presents a novel DA recognition approach for image documents, namely comic books. To the best of our knowledge, this is the first study investigating dialogue acts from comic books and represents the first steps to building a model for comic book understanding. The proposed method is composed of the following steps: speech balloon segmentation, optical character recognition (OCR), and DA recognition itself. We use YOLOv8 for balloon segmentation, Google Vision for OCR, and Transformer-based models for DA classification. The experiments are performed on a newly created dataset comprising 1,438 annotated comic panels. It contains bounding boxes, transcriptions, and dialogue act annotation. We have achieved nearly 98{\%} average precision for speech balloon segmentation and exceeded the accuracy of 70{\%} for the DA recognition task. We also present an analysis of dialogue structure in the comics domain and compare it with the standard DA datasets, representing another contribution of this paper.

COMICORDA: Dialogue Act Recognition in Comic Books

lrec-main.316

Poster

https://aclanthology.org/2024.lrec-main.317.bib

https://aclanthology.org/2024.lrec-main.317/

@inproceedings{rehm-etal-2024-common, title = "Common {E}uropean Language Data Space", author = {Rehm, Georg and Piperidis, Stelios and Choukri, Khalid and Vasi{\c{l}}jevs, Andrejs and Marheinecke, Katrin and Arranz, Victoria and B{\=e}rzi{\c{n}}{\v{s}}, Aivars and Deligiannis, Miltos and Galanis, Dimitris and Giagkou, Maria and Gkirtzou, Katerina and Gkoumas, Dimitris and Gr{\"u}tzner-Zahn, Annika and Kolovou, Athanasia and Labropoulou, Penny and Lagzdi{\c{n}}{\v{s}}, Andis and Leitner, Elena and Mapelli, Val{\'e}rie and Mazo, H{\'e}l{\`e}ne and Ostermann, Simon and Racioppa, Stefania and Rigault, Micka{\"e}l and Voukoutis, Leon}, editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.317", pages = "3579--3586", abstract = "The Common European Language Data Space (LDS) is an integral part of the EU data strategy, which aims at developing a single market for data. Its decentralised technical infrastructure and governance scheme are currently being developed by the LDS project, which also has dedicated tasks for proof-of-concept prototypes, handling legal aspects, raising awareness and promoting the LDS through events and social media channels. The LDS is part of a broader vision for establishing all necessary components to develop European large language models.", }

The Common European Language Data Space (LDS) is an integral part of the EU data strategy, which aims at developing a single market for data. Its decentralised technical infrastructure and governance scheme are currently being developed by the LDS project, which also has dedicated tasks for proof-of-concept prototypes, handling legal aspects, raising awareness and promoting the LDS through events and social media channels. The LDS is part of a broader vision for establishing all necessary components to develop European large language models.

Common European Language Data Space

lrec-main.317

Poster

https://aclanthology.org/2024.lrec-main.318.bib

https://aclanthology.org/2024.lrec-main.318/

@inproceedings{khebour-etal-2024-common, title = "Common Ground Tracking in Multimodal Dialogue", author = "Khebour, Ibrahim Khalil and Lai, Kenneth and Bradford, Mariah and Zhu, Yifan and Brutti, Richard A. and Tam, Christopher and Tu, Jingxuan and Ibarra, Benjamin A. and Blanchard, Nathaniel and Krishnaswamy, Nikhil and Pustejovsky, James", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.318", pages = "3587--3602", abstract = "Within Dialogue Modeling research in AI and NLP, considerable attention has been spent on {``}dialogue state tracking{''} (DST), which is the ability to update the representations of the speaker{'}s needs at each turn in the dialogue by taking into account the past dialogue moves and history. Less studied but just as important to dialogue modeling, however, is {``}common ground tracking{''} (CGT), which identifies the shared belief space held by all of the participants in a task-oriented dialogue: the task-relevant propositions all participants accept as true. In this paper we present a method for automatically identifying the current set of shared beliefs and {''}questions under discussion{''} (QUDs) of a group with a shared goal. We annotate a dataset of multimodal interactions in a shared physical space with speech transcriptions, prosodic features, gestures, actions, and facets of collaboration, and operationalize these features for use in a deep neural model to predict moves toward construction of common ground. Model outputs cascade into a set of formal closure rules derived from situated evidence and belief axioms and update operations. We empirically assess the contribution of each feature type toward successful construction of common ground relative to ground truth, establishing a benchmark in this novel, challenging task.", }

Within Dialogue Modeling research in AI and NLP, considerable attention has been spent on {``}dialogue state tracking{''} (DST), which is the ability to update the representations of the speaker{'}s needs at each turn in the dialogue by taking into account the past dialogue moves and history. Less studied but just as important to dialogue modeling, however, is {``}common ground tracking{''} (CGT), which identifies the shared belief space held by all of the participants in a task-oriented dialogue: the task-relevant propositions all participants accept as true. In this paper we present a method for automatically identifying the current set of shared beliefs and {''}questions under discussion{''} (QUDs) of a group with a shared goal. We annotate a dataset of multimodal interactions in a shared physical space with speech transcriptions, prosodic features, gestures, actions, and facets of collaboration, and operationalize these features for use in a deep neural model to predict moves toward construction of common ground. Model outputs cascade into a set of formal closure rules derived from situated evidence and belief axioms and update operations. We empirically assess the contribution of each feature type toward successful construction of common ground relative to ground truth, establishing a benchmark in this novel, challenging task.

Common Ground Tracking in Multimodal Dialogue

lrec-main.318

Poster

2403.17284

https://aclanthology.org/2024.lrec-main.319.bib

https://aclanthology.org/2024.lrec-main.319/

@inproceedings{imashev-etal-2024-comparative, title = "Comparative Analysis of Sign Language Interpreting Agents Perception: A Study of the Deaf", author = "Imashev, Alfarabi and Oralbayeva, Nurziya and Baizhanova, Gulmira and Sandygulova, Anara", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.319", pages = "3603--3609", abstract = "Prior research on sign language recognition has already demonstrated encouraging outcomes in achieving highly accurate and dependable automatic sign language recognition. The use of virtual characters as virtual assistants has significantly increased in the past decade. However, the progress in sign language generation and output that closely resembles physiologically believable human motions is still in its early stages. This assertion explains the lack of progress in virtual intelligent signing generative systems. Aside from the development of signing systems, scholarly research have revealed a significant deficiency in evaluating sign language generation systems by those who are deaf and use sign language. This paper presents the findings of a user study conducted with deaf signers. The study is aimed at comparing a state-of-the-art sign language generation system with a skilled sign language interpreter. The study focused on testing established metrics to gain insights into usability of such metrics for deaf signers and how deaf signers perceive signing agents.", }

Prior research on sign language recognition has already demonstrated encouraging outcomes in achieving highly accurate and dependable automatic sign language recognition. The use of virtual characters as virtual assistants has significantly increased in the past decade. However, the progress in sign language generation and output that closely resembles physiologically believable human motions is still in its early stages. This assertion explains the lack of progress in virtual intelligent signing generative systems. Aside from the development of signing systems, scholarly research have revealed a significant deficiency in evaluating sign language generation systems by those who are deaf and use sign language. This paper presents the findings of a user study conducted with deaf signers. The study is aimed at comparing a state-of-the-art sign language generation system with a skilled sign language interpreter. The study focused on testing established metrics to gain insights into usability of such metrics for deaf signers and how deaf signers perceive signing agents.

Comparative Analysis of Sign Language Interpreting Agents Perception: A Study of the Deaf

lrec-main.319

Poster

https://aclanthology.org/2024.lrec-main.320.bib

https://aclanthology.org/2024.lrec-main.320/

@inproceedings{pranav-etal-2024-comparing, title = "Comparing Static and Contextual Distributional Semantic Models on Intrinsic Tasks: An Evaluation on {M}andarin {C}hinese Datasets", author = "Pranav, A and Cong, Yan and Chersoni, Emmanuele and Hsu, Yu-Yin and Lenci, Alessandro", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.320", pages = "3610--3627", abstract = "The field of Distributional Semantics has recently undergone important changes, with the contextual representations produced by Transformers taking the place of static word embeddings models. Noticeably, previous studies comparing the two types of vectors have only focused on the English language and a limited number of models. In our study, we present a comparative evaluation of static and contextualized distributional models for Mandarin Chinese, focusing on a range of intrinsic tasks. Our results reveal that static models remain stronger for some of the classical tasks that consider word meaning independent of context, while contextualized models excel in identifying semantic relations between word pairs and in the categorization of words into abstract semantic classes.", }

The field of Distributional Semantics has recently undergone important changes, with the contextual representations produced by Transformers taking the place of static word embeddings models. Noticeably, previous studies comparing the two types of vectors have only focused on the English language and a limited number of models. In our study, we present a comparative evaluation of static and contextualized distributional models for Mandarin Chinese, focusing on a range of intrinsic tasks. Our results reveal that static models remain stronger for some of the classical tasks that consider word meaning independent of context, while contextualized models excel in identifying semantic relations between word pairs and in the categorization of words into abstract semantic classes.

Comparing Static and Contextual Distributional Semantic Models on Intrinsic Tasks: An Evaluation on Mandarin Chinese Datasets

lrec-main.320

Poster

https://aclanthology.org/2024.lrec-main.321.bib

https://aclanthology.org/2024.lrec-main.321/

@inproceedings{gimeno-gomez-martinez-hinarejos-2024-comparison, title = "Comparison of Conventional Hybrid and {CTC}/Attention Decoders for Continuous Visual Speech Recognition", author = "Gimeno-G{\'o}mez, David and Mart{\'\i}nez-Hinarejos, Carlos-D.", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.321", pages = "3628--3638", abstract = "Thanks to the rise of deep learning and the availability of large-scale audio-visual databases, recent advances have been achieved in Visual Speech Recognition (VSR). Similar to other speech processing tasks, these end-to-end VSR systems are usually based on encoder-decoder architectures. While encoders are somewhat general, multiple decoding approaches have been explored, such as the conventional hybrid model based on Deep Neural Networks combined with Hidden Markov Models (DNN-HMM) or the Connectionist Temporal Classification (CTC) paradigm. However, there are languages and tasks in which data is scarce, and in this situation, there is not a clear comparison between different types of decoders. Therefore, we focused our study on how the conventional DNN-HMM decoder and its state-of-the-art CTC/Attention counterpart behave depending on the amount of data used for their estimation. We also analyzed to what extent our visual speech features were able to adapt to scenarios for which they were not explicitly trained, either considering a similar dataset or another collected for a different language. Results showed that the conventional paradigm reached recognition rates that improve the CTC/Attention model in data-scarcity scenarios along with a reduced training time and fewer parameters.", }

Thanks to the rise of deep learning and the availability of large-scale audio-visual databases, recent advances have been achieved in Visual Speech Recognition (VSR). Similar to other speech processing tasks, these end-to-end VSR systems are usually based on encoder-decoder architectures. While encoders are somewhat general, multiple decoding approaches have been explored, such as the conventional hybrid model based on Deep Neural Networks combined with Hidden Markov Models (DNN-HMM) or the Connectionist Temporal Classification (CTC) paradigm. However, there are languages and tasks in which data is scarce, and in this situation, there is not a clear comparison between different types of decoders. Therefore, we focused our study on how the conventional DNN-HMM decoder and its state-of-the-art CTC/Attention counterpart behave depending on the amount of data used for their estimation. We also analyzed to what extent our visual speech features were able to adapt to scenarios for which they were not explicitly trained, either considering a similar dataset or another collected for a different language. Results showed that the conventional paradigm reached recognition rates that improve the CTC/Attention model in data-scarcity scenarios along with a reduced training time and fewer parameters.

Comparison of Conventional Hybrid and CTC/Attention Decoders for Continuous Visual Speech Recognition

lrec-main.321

Poster

2402.13004

https://aclanthology.org/2024.lrec-main.322.bib

https://aclanthology.org/2024.lrec-main.322/

@inproceedings{arimoto-etal-2024-comparison, title = "Comparison of the Intimacy Process between Real and Acting-based Long-term Text Chats", author = "Arimoto, Tsunehiro and Sugiyama, Hiroaki and Narimatsu, Hiromi and Mizukami, Masahiro", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.322", pages = "3639--3644", abstract = "Long-term chatbots are expected to develop relationships with users. The major trend in this field{'}s recent long-term chatbot studies is to train systems with virtual long-term chat data called Multi-Session Chat (MSC), which collects text chat from multiple sessions of crowd workers playing the roles of speakers with defined personas. However, no investigation has attempted to determine whether such virtual long-term chat can successfully simulate relationship-building between speakers. To clarify the difference between an actual long-term intimacy process and an MSC intimacy process, this study collects real long-term chat and MSC in Japanese and compares them in terms of speech form and dialogue acts. The results of analyzing these factors suggest that MSC have an unnatural tendency to behave as if they have a close relationship with non-polite speech levels compared to actual long-term chats, but also as if they have a shallow relationship with more questions than real long-term chats.", }

Long-term chatbots are expected to develop relationships with users. The major trend in this field{'}s recent long-term chatbot studies is to train systems with virtual long-term chat data called Multi-Session Chat (MSC), which collects text chat from multiple sessions of crowd workers playing the roles of speakers with defined personas. However, no investigation has attempted to determine whether such virtual long-term chat can successfully simulate relationship-building between speakers. To clarify the difference between an actual long-term intimacy process and an MSC intimacy process, this study collects real long-term chat and MSC in Japanese and compares them in terms of speech form and dialogue acts. The results of analyzing these factors suggest that MSC have an unnatural tendency to behave as if they have a close relationship with non-polite speech levels compared to actual long-term chats, but also as if they have a shallow relationship with more questions than real long-term chats.

Comparison of the Intimacy Process between Real and Acting-based Long-term Text Chats

lrec-main.322

Poster

https://aclanthology.org/2024.lrec-main.323.bib

https://aclanthology.org/2024.lrec-main.323/

@inproceedings{kelious-etal-2024-complex, title = "Complex Word Identification: A Comparative Study between {C}hat{GPT} and a Dedicated Model for This Task", author = "Kelious, Abdelhak and Constant, Mathieu and Coeur, Christophe", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.323", pages = "3645--3653", abstract = "There are several works in natural language processing for identifying lexical complexity. This can be for various reasons, either for simplification, the selection of more suitable content, or for other specific tasks. Words can have multiple definitions and degrees of complexity depending on the context in which they appear. One solution being investigated is lexical complexity prediction, where computational methods are used to evaluate the difficulty of vocabulary for language learners and offer personalized assistance. In this work, we explore deep learning methods to assess the complexity of a word based on its context. Specifically, we investigate how to use pre-trained language models to encode both the sentence and the target word, and then fine-tune them by combining them with additional frequency-based features. Our approach achieved superior results compared to the best systems in SemEval-2021 (Shardlow et al., 2021), as demonstrated by an R2 score of 0.65. Finally, we carry out a comparative study with ChatGPT to assess its potential for predicting lexical complexity, to see whether prompt engineering can be an alternative to this task, we will discuss the advantages and limitations of ChatGPT.", }

There are several works in natural language processing for identifying lexical complexity. This can be for various reasons, either for simplification, the selection of more suitable content, or for other specific tasks. Words can have multiple definitions and degrees of complexity depending on the context in which they appear. One solution being investigated is lexical complexity prediction, where computational methods are used to evaluate the difficulty of vocabulary for language learners and offer personalized assistance. In this work, we explore deep learning methods to assess the complexity of a word based on its context. Specifically, we investigate how to use pre-trained language models to encode both the sentence and the target word, and then fine-tune them by combining them with additional frequency-based features. Our approach achieved superior results compared to the best systems in SemEval-2021 (Shardlow et al., 2021), as demonstrated by an R2 score of 0.65. Finally, we carry out a comparative study with ChatGPT to assess its potential for predicting lexical complexity, to see whether prompt engineering can be an alternative to this task, we will discuss the advantages and limitations of ChatGPT.

Complex Word Identification: A Comparative Study between ChatGPT and a Dedicated Model for This Task

lrec-main.323

Poster

https://aclanthology.org/2024.lrec-main.324.bib

https://aclanthology.org/2024.lrec-main.324/

@inproceedings{idrissi-yaghir-etal-2024-comprehensive, title = "Comprehensive Study on {G}erman Language Models for Clinical and Biomedical Text Understanding", author = {Idrissi-Yaghir, Ahmad and Dada, Amin and Sch{\"a}fer, Henning and Arzideh, Kamyar and Baldini, Giulia and Trienes, Jan and Hasin, Max and Bewersdorff, Jeanette and Schmidt, Cynthia S. and Bauer, Marie and Smith, Kaleb E. and Bian, Jiang and Wu, Yonghui and Schl{\"o}tterer, J{\"o}rg and Zesch, Torsten and Horn, Peter A. and Seifert, Christin and Nensa, Felix and Kleesiek, Jens and Friedrich, Christoph M.}, editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.324", pages = "3654--3665", abstract = "Recent advances in natural language processing (NLP) can be largely attributed to the advent of pre-trained language models such as BERT and RoBERTa. While these models demonstrate remarkable performance on general datasets, they can struggle in specialized domains such as medicine, where unique domain-specific terminologies, domain-specific abbreviations, and varying document structures are common. This paper explores strategies for adapting these models to domain-specific requirements, primarily through continuous pre-training on domain-specific data. We pre-trained several German medical language models on 2.4B tokens derived from translated public English medical data and 3B tokens of German clinical data. The resulting models were evaluated on various German downstream tasks, including named entity recognition (NER), multi-label classification, and extractive question answering. Our results suggest that models augmented by clinical and translation-based pre-training typically outperform general domain models in medical contexts. We conclude that continuous pre-training has demonstrated the ability to match or even exceed the performance of clinical models trained from scratch. Furthermore, pre-training on clinical data or leveraging translated texts have proven to be reliable methods for domain adaptation in medical NLP tasks.", }

Recent advances in natural language processing (NLP) can be largely attributed to the advent of pre-trained language models such as BERT and RoBERTa. While these models demonstrate remarkable performance on general datasets, they can struggle in specialized domains such as medicine, where unique domain-specific terminologies, domain-specific abbreviations, and varying document structures are common. This paper explores strategies for adapting these models to domain-specific requirements, primarily through continuous pre-training on domain-specific data. We pre-trained several German medical language models on 2.4B tokens derived from translated public English medical data and 3B tokens of German clinical data. The resulting models were evaluated on various German downstream tasks, including named entity recognition (NER), multi-label classification, and extractive question answering. Our results suggest that models augmented by clinical and translation-based pre-training typically outperform general domain models in medical contexts. We conclude that continuous pre-training has demonstrated the ability to match or even exceed the performance of clinical models trained from scratch. Furthermore, pre-training on clinical data or leveraging translated texts have proven to be reliable methods for domain adaptation in medical NLP tasks.

Comprehensive Study on German Language Models for Clinical and Biomedical Text Understanding

lrec-main.324

Poster

2404.05694

https://huggingface.co/papers/2404.05694

https://aclanthology.org/2024.lrec-main.325.bib

https://aclanthology.org/2024.lrec-main.325/

@inproceedings{liu-etal-2024-computational, title = "Computational Modelling of Plurality and Definiteness in {C}hinese Noun Phrases", author = "Liu, Yuqi and Chen, Guanyi and van Deemter, Kees", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.325", pages = "3666--3676", abstract = "Theoretical linguists have suggested that some languages (e.g., Chinese and Japanese) are {``}cooler{''} than other languages based on the observation that the intended meaning of phrases in these languages depends more on their contexts. As a result, many expressions in these languages are shortened, and their meaning is inferred from the context. In this paper, we focus on the omission of the plurality and definiteness markers in Chinese noun phrases (NPs) to investigate the predictability of their intended meaning given the contexts. To this end, we built a corpus of Chinese NPs, each of which is accompanied by its corresponding context, and by labels indicating its singularity/plurality and definiteness/indefiniteness. We carried out corpus assessments and analyses. The results suggest that Chinese speakers indeed drop plurality and definiteness markers very frequently. Building on the corpus, we train a bank of computational models using both classic machine learning models and state-of-the-art pre-trained language models to predict the plurality and definiteness of each NP. We report on the performance of these models and analyse their behaviours.", }

Theoretical linguists have suggested that some languages (e.g., Chinese and Japanese) are {``}cooler{''} than other languages based on the observation that the intended meaning of phrases in these languages depends more on their contexts. As a result, many expressions in these languages are shortened, and their meaning is inferred from the context. In this paper, we focus on the omission of the plurality and definiteness markers in Chinese noun phrases (NPs) to investigate the predictability of their intended meaning given the contexts. To this end, we built a corpus of Chinese NPs, each of which is accompanied by its corresponding context, and by labels indicating its singularity/plurality and definiteness/indefiniteness. We carried out corpus assessments and analyses. The results suggest that Chinese speakers indeed drop plurality and definiteness markers very frequently. Building on the corpus, we train a bank of computational models using both classic machine learning models and state-of-the-art pre-trained language models to predict the plurality and definiteness of each NP. We report on the performance of these models and analyse their behaviours.

Computational Modelling of Plurality and Definiteness in Chinese Noun Phrases

lrec-main.325

Poster

2403.04376

https://aclanthology.org/2024.lrec-main.326.bib

https://aclanthology.org/2024.lrec-main.326/

@inproceedings{vallecillo-rodriguez-etal-2024-conan, title = "{CONAN}-{MT}-{SP}: A {S}panish Corpus for Counternarrative Using {GPT} Models", author = "Vallecillo Rodr{\'\i}guez, Mar{\'\i}a Estrella and Cantero Romero, Maria Victoria and Cabrera De Castro, Isabel and Montejo R{\'a}ez, Arturo and Mart{\'\i}n Valdivia, Mar{\'\i}a Teresa", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.326", pages = "3677--3688", abstract = "This paper describes the automated generation of CounterNarratives (CNs) for Hate Speech (HS) in Spanish using GPT-based models. Our primary objective is to evaluate the performance of these models in comparison to human capabilities. For this purpose, the English CONAN Multitarget corpus is taken as a starting point and we use the DeepL API to automatically translate into Spanish. Two GPT-based models, GPT-3 and GPT-4, are applied to the HS segment through a few-shot prompting strategy to generate a new CN. As a consequence of our research, we have created a high quality corpus in Spanish that includes the original HS-CN pairs translated into Spanish, in addition to the CNs generated automatically with the GPT models and that have been evaluated manually. The resulting CONAN-MT-SP corpus and its evaluation will be made available to the research community, representing the most extensive linguistic resource of CNs in Spanish to date. The results demonstrate that, although the effectiveness of GPT-4 outperforms GPT-3, both models can be used as systems to automatically generate CNs to combat the HS. Moreover, these models consistently outperform human performance in most instances.", }

This paper describes the automated generation of CounterNarratives (CNs) for Hate Speech (HS) in Spanish using GPT-based models. Our primary objective is to evaluate the performance of these models in comparison to human capabilities. For this purpose, the English CONAN Multitarget corpus is taken as a starting point and we use the DeepL API to automatically translate into Spanish. Two GPT-based models, GPT-3 and GPT-4, are applied to the HS segment through a few-shot prompting strategy to generate a new CN. As a consequence of our research, we have created a high quality corpus in Spanish that includes the original HS-CN pairs translated into Spanish, in addition to the CNs generated automatically with the GPT models and that have been evaluated manually. The resulting CONAN-MT-SP corpus and its evaluation will be made available to the research community, representing the most extensive linguistic resource of CNs in Spanish to date. The results demonstrate that, although the effectiveness of GPT-4 outperforms GPT-3, both models can be used as systems to automatically generate CNs to combat the HS. Moreover, these models consistently outperform human performance in most instances.

CONAN-MT-SP: A Spanish Corpus for Counternarrative Using GPT Models

lrec-main.326

Poster

https://aclanthology.org/2024.lrec-main.327.bib

https://aclanthology.org/2024.lrec-main.327/

@inproceedings{hough-etal-2024-conceptual, title = "Conceptual Pacts for Reference Resolution Using Small, Dynamically Constructed Language Models: A Study in Puzzle Building Dialogues", author = "Hough, Julian and Zarrie{\ss}, Sina and Kennington, Casey and Schlangen, David and Poesio, Massimo", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.327", pages = "3689--3699", abstract = "Using Brennan and Clark{'}s theory of a Conceptual Pact, that when interlocutors agree on a name for an object, they are forming a temporary agreement on how to conceptualize that object, we present an extension to a simple reference resolver which simulates this process over time with different conversation pairs. In a puzzle construction domain, we model pacts with small language models for each referent which update during the interaction. When features from these pact models are incorporated into a simple bag-of-words reference resolver, the accuracy increases compared to using a standard pre-trained model. The model performs equally to a competitor using the same data but with exhaustive re-training after each prediction, while also being more transparent, faster and less resource-intensive. We also experiment with reducing the number of training interactions, and can still achieve reference resolution accuracies of over 80{\%} in testing from observing a single previous interaction, over 20{\%} higher than a pre-trained baseline. While this is a limited domain, we argue the model could be applicable to larger real-world applications in human and human-robot interaction and is an interpretable and transparent model.", }

Using Brennan and Clark{'}s theory of a Conceptual Pact, that when interlocutors agree on a name for an object, they are forming a temporary agreement on how to conceptualize that object, we present an extension to a simple reference resolver which simulates this process over time with different conversation pairs. In a puzzle construction domain, we model pacts with small language models for each referent which update during the interaction. When features from these pact models are incorporated into a simple bag-of-words reference resolver, the accuracy increases compared to using a standard pre-trained model. The model performs equally to a competitor using the same data but with exhaustive re-training after each prediction, while also being more transparent, faster and less resource-intensive. We also experiment with reducing the number of training interactions, and can still achieve reference resolution accuracies of over 80{\%} in testing from observing a single previous interaction, over 20{\%} higher than a pre-trained baseline. While this is a limited domain, we argue the model could be applicable to larger real-world applications in human and human-robot interaction and is an interpretable and transparent model.

Conceptual Pacts for Reference Resolution Using Small, Dynamically Constructed Language Models: A Study in Puzzle Building Dialogues

lrec-main.327

Poster

https://aclanthology.org/2024.lrec-main.328.bib

https://aclanthology.org/2024.lrec-main.328/

@inproceedings{huang-etal-2024-conec, title = "{C}on{EC}: Earnings Call Dataset with Real-world Contexts for Benchmarking Contextual Speech Recognition", author = "Huang, Ruizhe and Yarmohammadi, Mahsa and Trmal, Jan and Liu, Jing and Raj, Desh and Garcia, Leibny Paola and Ivanov, Alexei V. and Ehlen, Patrick and Yu, Mingzhi and Povey, Dan and Khudanpur, Sanjeev", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.328", pages = "3700--3706", abstract = "Knowing the particular context associated with a conversation can help improving the performance of an automatic speech recognition (ASR) system. For example, if we are provided with a list of in-context words or phrases {---} such as the speaker{'}s contacts or recent song playlists {---} during inference, we can bias the recognition process towards this list. There are many works addressing contextual ASR; however, there is few publicly available real benchmark for evaluation, making it difficult to compare different solutions. To this end, we provide a corpus ({``}ConEC{''}) and baselines to evaluate contextual ASR approaches, grounded on real-world applications. The ConEC corpus is based on public-domain earnings calls (ECs) and associated supplementary materials, such as presentation slides, earnings news release as well as a list of meeting participants{'} names and affiliations. We demonstrate that such real contexts are noisier than artificially synthesized contexts that contain the ground truth, yet they still make great room for future improvement of contextual ASR technology", }

Knowing the particular context associated with a conversation can help improving the performance of an automatic speech recognition (ASR) system. For example, if we are provided with a list of in-context words or phrases {---} such as the speaker{'}s contacts or recent song playlists {---} during inference, we can bias the recognition process towards this list. There are many works addressing contextual ASR; however, there is few publicly available real benchmark for evaluation, making it difficult to compare different solutions. To this end, we provide a corpus ({``}ConEC{''}) and baselines to evaluate contextual ASR approaches, grounded on real-world applications. The ConEC corpus is based on public-domain earnings calls (ECs) and associated supplementary materials, such as presentation slides, earnings news release as well as a list of meeting participants{'} names and affiliations. We demonstrate that such real contexts are noisier than artificially synthesized contexts that contain the ground truth, yet they still make great room for future improvement of contextual ASR technology

ConEC: Earnings Call Dataset with Real-world Contexts for Benchmarking Contextual Speech Recognition

lrec-main.328

Poster

https://aclanthology.org/2024.lrec-main.329.bib

https://aclanthology.org/2024.lrec-main.329/

@inproceedings{han-etal-2024-conjoin, title = "Conjoin after Decompose: Improving Few-Shot Performance of Named Entity Recognition", author = "Han, Chengcheng and Zhu, Renyu and Kuang, Jun and Chen, Fengjiao and Li, Xiang and Gao, Ming and Cao, Xuezhi and Xian, Yunsen", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.329", pages = "3707--3717", abstract = "Prompt-based methods have been widely used in few-shot named entity recognition (NER). In this paper, we first conduct a preliminary experiment and observe that the key to affecting the performance of prompt-based NER models is the capability to detect entity boundaries. However, most existing models fail to boost such capability. To solve the issue, we propose a novel model, ParaBART, which consists of a BART encoder and a specially designed parabiotic decoder. Specifically, the parabiotic decoder includes two BART decoders and a conjoint module. The two decoders are responsible for entity boundary detection and entity type classification, respectively. They are connected by the conjoint module, which is used to replace unimportant tokens{'} embeddings in one decoder with the average embedding of all the tokens in the other. We further present a novel boundary expansion strategy to enhance the model{'}s capability in entity type classification. Experimental results show that ParaBART can achieve significant performance gains over state-of-the-art competitors.", }

Prompt-based methods have been widely used in few-shot named entity recognition (NER). In this paper, we first conduct a preliminary experiment and observe that the key to affecting the performance of prompt-based NER models is the capability to detect entity boundaries. However, most existing models fail to boost such capability. To solve the issue, we propose a novel model, ParaBART, which consists of a BART encoder and a specially designed parabiotic decoder. Specifically, the parabiotic decoder includes two BART decoders and a conjoint module. The two decoders are responsible for entity boundary detection and entity type classification, respectively. They are connected by the conjoint module, which is used to replace unimportant tokens{'} embeddings in one decoder with the average embedding of all the tokens in the other. We further present a novel boundary expansion strategy to enhance the model{'}s capability in entity type classification. Experimental results show that ParaBART can achieve significant performance gains over state-of-the-art competitors.

Conjoin after Decompose: Improving Few-Shot Performance of Named Entity Recognition

lrec-main.329

Poster

https://aclanthology.org/2024.lrec-main.330.bib

https://aclanthology.org/2024.lrec-main.330/

@inproceedings{rueda-etal-2024-conll, title = "{C}o{NLL}{\#}: Fine-grained Error Analysis and a Corrected Test Set for {C}o{NLL}-03 {E}nglish", author = "Rueda, Andrew and Alvarez-Mellado, Elena and Lignos, Constantine", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.330", pages = "3718--3728", abstract = "Modern named entity recognition systems have steadily improved performance in the age of larger and more powerful neural models. However, over the past several years, the state-of-the-art has seemingly hit another plateau on the benchmark CoNLL-03 English dataset. In this paper, we perform a deep dive into the test outputs of the highest-performing NER models, conducting a fine-grained evaluation of their performance by introducing new document-level annotations on the test set. We go beyond F1 scores by categorizing errors in order to interpret the true state of the art for NER and guide future work. We review previous attempts at correcting the various flaws of the test set and introduce CoNLL{\#}, a new corrected version of the test set that addresses its systematic and most prevalent errors, allowing for low-noise, interpretable error analysis.", }

Modern named entity recognition systems have steadily improved performance in the age of larger and more powerful neural models. However, over the past several years, the state-of-the-art has seemingly hit another plateau on the benchmark CoNLL-03 English dataset. In this paper, we perform a deep dive into the test outputs of the highest-performing NER models, conducting a fine-grained evaluation of their performance by introducing new document-level annotations on the test set. We go beyond F1 scores by categorizing errors in order to interpret the true state of the art for NER and guide future work. We review previous attempts at correcting the various flaws of the test set and introduce CoNLL{\#}, a new corrected version of the test set that addresses its systematic and most prevalent errors, allowing for low-noise, interpretable error analysis.

CoNLL#: Fine-grained Error Analysis and a Corrected Test Set for CoNLL-03 English

lrec-main.330

Poster

2405.11865

https://aclanthology.org/2024.lrec-main.331.bib

https://aclanthology.org/2024.lrec-main.331/

@inproceedings{van-esch-etal-2024-connecting, title = "Connecting Language Technologies with Rich, Diverse Data Sources Covering Thousands of Languages", author = "van Esch, Daan and Ritchie, Sandy and Ruder, Sebastian and Kreutzer, Julia and Rivera, Clara and Saxena, Ishank and Caswell, Isaac", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.331", pages = "3729--3746", abstract = "Contrary to common belief, there are rich and diverse data sources available for many thousands of languages, which can be used to develop technologies for these languages. In this paper, we provide an overview of some of the major online data sources, the types of data that they provide access to, potential applications of this data, and the number of languages that they cover. Even this covers only a small fraction of the data that exists; for example, printed books are published in many languages but few online aggregators exist.", }

Contrary to common belief, there are rich and diverse data sources available for many thousands of languages, which can be used to develop technologies for these languages. In this paper, we provide an overview of some of the major online data sources, the types of data that they provide access to, potential applications of this data, and the number of languages that they cover. Even this covers only a small fraction of the data that exists; for example, printed books are published in many languages but few online aggregators exist.

Connecting Language Technologies with Rich, Diverse Data Sources Covering Thousands of Languages

lrec-main.331

Poster

https://aclanthology.org/2024.lrec-main.332.bib

https://aclanthology.org/2024.lrec-main.332/

@inproceedings{sung-shin-2024-constructing, title = "Constructing a Dependency Treebank for Second Language Learners of {K}orean", author = "Sung, Hakyung and Shin, Gyu-Ho", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.332", pages = "3747--3758", abstract = "We introduce a manually annotated syntactic treebank based on Universal Dependencies, derived from the written data of second language (L2) Korean learners. In developing this new dataset, we critically evaluated previous works and revised the annotation guidelines to better reflect the linguistic properties of Korean and the characteristics of L2 learners. The L2 Korean treebank encompasses 7,530 sentences (66,982 words; 129,333 morphemes) and is publicly available at: https://github.com/NLPxL2Korean/L2KW-corpus.", }

We introduce a manually annotated syntactic treebank based on Universal Dependencies, derived from the written data of second language (L2) Korean learners. In developing this new dataset, we critically evaluated previous works and revised the annotation guidelines to better reflect the linguistic properties of Korean and the characteristics of L2 learners. The L2 Korean treebank encompasses 7,530 sentences (66,982 words; 129,333 morphemes) and is publicly available at: https://github.com/NLPxL2Korean/L2KW-corpus.

Constructing a Dependency Treebank for Second Language Learners of Korean

lrec-main.332

Poster

https://aclanthology.org/2024.lrec-main.333.bib

https://aclanthology.org/2024.lrec-main.333/

@inproceedings{kardinata-etal-2024-constructing, title = "Constructing {I}ndonesian-{E}nglish Travelogue Dataset", author = "Kardinata, Eunike Andriani and Ouchi, Hiroki and Watanabe, Taro", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.333", pages = "3759--3771", abstract = "Research in low-resource language is often hampered due to the under-representation of how the language is being used in reality. This is particularly true for Indonesian language because there is a limited variety of textual datasets, and majority were acquired from official sources with formal writing style. All the more for the task of geoparsing, which could be implemented for navigation and travel planning applications, such datasets are rare, even in the high-resource languages, such as English. Being aware of the need for a new resource in both languages for this specific task, we constructed a new dataset comprising both Indonesian and English from personal travelogue articles. Our dataset consists of 88 articles, exactly half of them written in each language. We covered both named and nominal expressions of four entity types related to travel: location, facility, transportation, and line. We also conducted experiments by training classifiers to recognise named entities and their nominal expressions. The results of our experiments showed a promising future use of our dataset as we obtained F1-score above 0.9 for both languages.", }

Research in low-resource language is often hampered due to the under-representation of how the language is being used in reality. This is particularly true for Indonesian language because there is a limited variety of textual datasets, and majority were acquired from official sources with formal writing style. All the more for the task of geoparsing, which could be implemented for navigation and travel planning applications, such datasets are rare, even in the high-resource languages, such as English. Being aware of the need for a new resource in both languages for this specific task, we constructed a new dataset comprising both Indonesian and English from personal travelogue articles. Our dataset consists of 88 articles, exactly half of them written in each language. We covered both named and nominal expressions of four entity types related to travel: location, facility, transportation, and line. We also conducted experiments by training classifiers to recognise named entities and their nominal expressions. The results of our experiments showed a promising future use of our dataset as we obtained F1-score above 0.9 for both languages.

Constructing Indonesian-English Travelogue Dataset

lrec-main.333

Poster

https://aclanthology.org/2024.lrec-main.334.bib

https://aclanthology.org/2024.lrec-main.334/

@inproceedings{han-etal-2024-constructing, title = "Constructing {K}orean Learners{'} {L}2 Speech Corpus of Seven Languages for Automatic Pronunciation Assessment", author = "Han, Seunghee and Kim, Sunhee and Chung, Minhwa", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.334", pages = "3772--3781", abstract = "Multilingual L2 speech corpora for developing automatic speech assessment are currently available, but they lack comprehensive annotations of L2 speech from non-native speakers of various languages. This study introduces the methodology of designing a Korean learners{'} L2 speech corpus of seven languages: English, Japanese, Chinese, French, German, Spanish, and Russian. We describe the development of reading scripts, reading tasks, scoring criteria, and expert evaluation methods in detail. Our corpus contains 1,200 hours of L2 speech data from Korean learners (400 hours for English, 200 hours each for Japanese and Chinese, 100 hours each for French, German, Spanish, and Russian). The corpus is annotated with spelling and pronunciation transcription, expert pronunciation assessment scores (accuracy of pronunciation and fluency of prosody), and metadata such as gender, age, self-reported language proficiency, and pronunciation error types. We also propose a practical verification method and a reliability threshold to ensure the reliability and objectivity of large-scale subjective evaluation data.", }

Multilingual L2 speech corpora for developing automatic speech assessment are currently available, but they lack comprehensive annotations of L2 speech from non-native speakers of various languages. This study introduces the methodology of designing a Korean learners{'} L2 speech corpus of seven languages: English, Japanese, Chinese, French, German, Spanish, and Russian. We describe the development of reading scripts, reading tasks, scoring criteria, and expert evaluation methods in detail. Our corpus contains 1,200 hours of L2 speech data from Korean learners (400 hours for English, 200 hours each for Japanese and Chinese, 100 hours each for French, German, Spanish, and Russian). The corpus is annotated with spelling and pronunciation transcription, expert pronunciation assessment scores (accuracy of pronunciation and fluency of prosody), and metadata such as gender, age, self-reported language proficiency, and pronunciation error types. We also propose a practical verification method and a reliability threshold to ensure the reliability and objectivity of large-scale subjective evaluation data.

Constructing Korean Learners' L2 Speech Corpus of Seven Languages for Automatic Pronunciation Assessment

lrec-main.334

Poster

https://aclanthology.org/2024.lrec-main.335.bib

https://aclanthology.org/2024.lrec-main.335/

@inproceedings{mousavi-etal-2024-construction, title = "Construction of Paired Knowledge Graph - Text Datasets Informed by Cyclic Evaluation", author = "Mousavi, Ali and Zhan, Xin and Bai, He and Shi, Peng and Rekatsinas, Theodoros and Han, Benjamin and Li, Yunyao and Pound, Jeffrey and Susskind, Joshua M. and Schluter, Natalie and Ilyas, Ihab F. and Jaitly, Navdeep", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.335", pages = "3782--3803", abstract = "Datasets that pair Knowledge Graphs (KG) and text together (KG-T) can be used to train forward and reverse neural models that generate text from KG and vice versa. However models trained on datasets where KG and text pairs are not equivalent can suffer from more hallucination and poorer recall. In this paper, we verify this empirically by generating datasets with different levels of noise and find that noisier datasets do indeed lead to more hallucination. We argue that the ability of forward and reverse models trained on a dataset to cyclically regenerate source KG or text is a proxy for the equivalence between the KG and the text in the dataset. Using cyclic evaluation we find that manually created WebNLG is much better than automatically created TeKGen and T-REx. Informed by these observations, we construct a new, improved dataset called \textbf{LAGRANGE} using heuristics meant to improve equivalence between KG and text and show the impact of each of the heuristics on cyclic evaluation. We also construct two synthetic datasets using large language models (LLMs), and observe that these are conducive to models that perform significantly well on cyclic generation of text, but less so on cyclic generation of KGs, probably because of a lack of a consistent underlying ontology.", }

Datasets that pair Knowledge Graphs (KG) and text together (KG-T) can be used to train forward and reverse neural models that generate text from KG and vice versa. However models trained on datasets where KG and text pairs are not equivalent can suffer from more hallucination and poorer recall. In this paper, we verify this empirically by generating datasets with different levels of noise and find that noisier datasets do indeed lead to more hallucination. We argue that the ability of forward and reverse models trained on a dataset to cyclically regenerate source KG or text is a proxy for the equivalence between the KG and the text in the dataset. Using cyclic evaluation we find that manually created WebNLG is much better than automatically created TeKGen and T-REx. Informed by these observations, we construct a new, improved dataset called \textbf{LAGRANGE} using heuristics meant to improve equivalence between KG and text and show the impact of each of the heuristics on cyclic evaluation. We also construct two synthetic datasets using large language models (LLMs), and observe that these are conducive to models that perform significantly well on cyclic generation of text, but less so on cyclic generation of KGs, probably because of a lack of a consistent underlying ontology.

Construction of Paired Knowledge Graph - Text Datasets Informed by Cyclic Evaluation

lrec-main.335

Poster

https://aclanthology.org/2024.lrec-main.336.bib

https://aclanthology.org/2024.lrec-main.336/

@inproceedings{zhou-etal-2024-constructions, title = "Constructions Are So Difficult That {E}ven Large Language Models Get Them Right for the Wrong Reasons", author = {Zhou, Shijia and Weissweiler, Leonie and He, Taiqi and Sch{\"u}tze, Hinrich and Mortensen, David R. and Levin, Lori}, editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.336", pages = "3804--3811", abstract = "In this paper, we make a contribution that can be understood from two perspectives: from an NLP perspective, we introduce a small challenge dataset for NLI with large lexical overlap, which minimises the possibility of models discerning entailment solely based on token distinctions, and show that GPT-4 and Llama 2 fail it with strong bias. We then create further challenging sub-tasks in an effort to explain this failure. From a Computational Linguistics perspective, we identify a group of constructions with three classes of adjectives which cannot be distinguished by surface features. This enables us to probe for LLM{'}s understanding of these constructions in various ways, and we find that they fail in a variety of ways to distinguish between them, suggesting that they don{'}t adequately represent their meaning or capture the lexical properties of phrasal heads.", }

In this paper, we make a contribution that can be understood from two perspectives: from an NLP perspective, we introduce a small challenge dataset for NLI with large lexical overlap, which minimises the possibility of models discerning entailment solely based on token distinctions, and show that GPT-4 and Llama 2 fail it with strong bias. We then create further challenging sub-tasks in an effort to explain this failure. From a Computational Linguistics perspective, we identify a group of constructions with three classes of adjectives which cannot be distinguished by surface features. This enables us to probe for LLM{'}s understanding of these constructions in various ways, and we find that they fail in a variety of ways to distinguish between them, suggesting that they don{'}t adequately represent their meaning or capture the lexical properties of phrasal heads.

Constructions Are So Difficult That Even Large Language Models Get Them Right for the Wrong Reasons

lrec-main.336

Poster

2403.17760

https://aclanthology.org/2024.lrec-main.337.bib

https://aclanthology.org/2024.lrec-main.337/

@inproceedings{yu-etal-2024-context, title = "Context-Aware Non-Autoregressive Document-Level Translation with Sentence-Aligned Connectionist Temporal Classification", author = "Yu, Hao and Huang, Kaiyu and Zhao, Anqi and Liu, Junpeng and Huang, Degen", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.337", pages = "3812--3824", abstract = "Previous studies employ the autoregressive translation (AT) paradigm in the document-to-document neural machine translation. These methods extend the translation unit from a single sentence to a pseudo-document and encodes the full pseudo-document, avoiding the redundant computation problem in context. However, the AT methods cannot parallelize decoding and struggle with error accumulation, especially when the length of sentences increases. In this work, we propose a context-aware non-autoregressive framework with the sentence-aligned connectionist temporal classification (SA-CTC) loss for document-level neural machine translation. In particular, the SA-CTC loss reduces the search space of the decoding path by fixing the positions of the beginning and end tokens for each sentence in the document. Meanwhile, the context-aware architecture introduces preset nodes to represent sentence-level information and utilizes a hierarchical attention structure to regulate the attention hypothesis space. Experimental results show that our proposed method can achieve competitive performance compared with several strong baselines. Our method implements non-autoregressive modeling in Doc-to-Doc translation manner, achieving an average 46X decoding speedup compared to the document-level AT baselines on three benchmarks.", }

Previous studies employ the autoregressive translation (AT) paradigm in the document-to-document neural machine translation. These methods extend the translation unit from a single sentence to a pseudo-document and encodes the full pseudo-document, avoiding the redundant computation problem in context. However, the AT methods cannot parallelize decoding and struggle with error accumulation, especially when the length of sentences increases. In this work, we propose a context-aware non-autoregressive framework with the sentence-aligned connectionist temporal classification (SA-CTC) loss for document-level neural machine translation. In particular, the SA-CTC loss reduces the search space of the decoding path by fixing the positions of the beginning and end tokens for each sentence in the document. Meanwhile, the context-aware architecture introduces preset nodes to represent sentence-level information and utilizes a hierarchical attention structure to regulate the attention hypothesis space. Experimental results show that our proposed method can achieve competitive performance compared with several strong baselines. Our method implements non-autoregressive modeling in Doc-to-Doc translation manner, achieving an average 46X decoding speedup compared to the document-level AT baselines on three benchmarks.

Context-Aware Non-Autoregressive Document-Level Translation with Sentence-Aligned Connectionist Temporal Classification

lrec-main.337

Poster

https://aclanthology.org/2024.lrec-main.338.bib

https://aclanthology.org/2024.lrec-main.338/

@inproceedings{goren-strapparava-2024-context, title = "Context Matters: Enhancing Metaphor Recognition in Proverbs", author = "Goren, Gamze and Strapparava, Carlo", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.338", pages = "3825--3830", abstract = "Despite the remarkable achievements of Large Language Models (LLMs) in various Natural Language Processing tasks, their competence in abstract language understanding remains a relatively under-explored territory. Figurative language interpretation serves as ideal testbed for assessing this as it requires models to navigate beyond the literal meaning and delve into underlying semantics of the figurative expressions. In this paper, we seek to examine the performance of GPT-3.5 in zero-shot setting through word-level metaphor detection. Specifically, we frame the task as annotation of word-level metaphors in proverbs. To this end, we employ a dataset of English proverbs and evaluated its performance by applying different prompting strategies. Our results show that the model shows a satisfactory performance at identifying word-level metaphors, particularly when it is prompted with a hypothetical context preceding the proverb. This observation underscores the pivotal role of well-designed prompts for zero-shot settings through which these models can be leveraged as annotators for subjective NLP tasks.", }

Despite the remarkable achievements of Large Language Models (LLMs) in various Natural Language Processing tasks, their competence in abstract language understanding remains a relatively under-explored territory. Figurative language interpretation serves as ideal testbed for assessing this as it requires models to navigate beyond the literal meaning and delve into underlying semantics of the figurative expressions. In this paper, we seek to examine the performance of GPT-3.5 in zero-shot setting through word-level metaphor detection. Specifically, we frame the task as annotation of word-level metaphors in proverbs. To this end, we employ a dataset of English proverbs and evaluated its performance by applying different prompting strategies. Our results show that the model shows a satisfactory performance at identifying word-level metaphors, particularly when it is prompted with a hypothetical context preceding the proverb. This observation underscores the pivotal role of well-designed prompts for zero-shot settings through which these models can be leveraged as annotators for subjective NLP tasks.

Context Matters: Enhancing Metaphor Recognition in Proverbs

lrec-main.338

Poster

https://aclanthology.org/2024.lrec-main.339.bib

https://aclanthology.org/2024.lrec-main.339/

@inproceedings{kobrock-etal-2024-context, title = "Context Shapes Emergent Communication about Concepts at Different Levels of Abstraction", author = "Kobrock, Kristina and Ohmer, Xenia Isabel and Bruni, Elia and Gotzner, Nicole", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.339", pages = "3831--3848", abstract = "We study the communication of concepts at different levels of abstraction and in different contexts in an agent-based, interactive reference game. While playing the concept-level reference game, the neural network agents develop a communication system from scratch. We use a novel symbolic dataset that disentangles concept type (ranging from specific to generic) and context (ranging from fine to coarse) to study the influence of these factors on the emerging language. We compare two game scenarios: one in which speaker agents have access to context information (context-aware) and one in which the speaker agents do not have access to context information (context-unaware). First, we find that the agents learn higher-level concepts from the object inputs alone. Second, an analysis of the emergent communication system shows that only context-aware agents learn to communicate efficiently by adapting their messages to the context conditions and relying on context for unambiguous reference. Crucially, this behavior is not explicitly incentivized by the game, but efficient communication emerges and is driven by the availability of context alone. The emerging language we observe is reminiscent of evolutionary pressures on human languages and highlights the pivotal role of context in a communication system.", }

We study the communication of concepts at different levels of abstraction and in different contexts in an agent-based, interactive reference game. While playing the concept-level reference game, the neural network agents develop a communication system from scratch. We use a novel symbolic dataset that disentangles concept type (ranging from specific to generic) and context (ranging from fine to coarse) to study the influence of these factors on the emerging language. We compare two game scenarios: one in which speaker agents have access to context information (context-aware) and one in which the speaker agents do not have access to context information (context-unaware). First, we find that the agents learn higher-level concepts from the object inputs alone. Second, an analysis of the emergent communication system shows that only context-aware agents learn to communicate efficiently by adapting their messages to the context conditions and relying on context for unambiguous reference. Crucially, this behavior is not explicitly incentivized by the game, but efficient communication emerges and is driven by the availability of context alone. The emerging language we observe is reminiscent of evolutionary pressures on human languages and highlights the pivotal role of context in a communication system.

Context Shapes Emergent Communication about Concepts at Different Levels of Abstraction

lrec-main.339

Poster

https://aclanthology.org/2024.lrec-main.340.bib

https://aclanthology.org/2024.lrec-main.340/

@inproceedings{mandal-etal-2024-contextualizing, title = "Contextualizing Generated Citation Texts", author = "Mandal, Biswadip and Li, Xiangci and Ouyang, Jessica", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.340", pages = "3849--3854", abstract = "Abstractive citation text generation is usually framed as an infilling task, where a sequence-to-sequence model is trained to generate a citation given a reference paper and the context window around the target; the generated citation should be a brief discussion of the reference paper as it relates to the citing context. However, examining a recent LED-based citation generation system, we find that many of the generated citations are generic summaries of the reference paper{'}s main contribution, ignoring the citation context{'}s focus on a different topic. To address this problem, we propose a simple modification to the citation text generation task: the generation target is not only the citation itself, but the entire context window, including the target citation. This approach can be easily applied to any abstractive citation generation system, and our experimental results show that training in this way is preferred by human readers and allows the generation model to make use of contextual clues about what topic to discuss and what stance to take.", }

Abstractive citation text generation is usually framed as an infilling task, where a sequence-to-sequence model is trained to generate a citation given a reference paper and the context window around the target; the generated citation should be a brief discussion of the reference paper as it relates to the citing context. However, examining a recent LED-based citation generation system, we find that many of the generated citations are generic summaries of the reference paper{'}s main contribution, ignoring the citation context{'}s focus on a different topic. To address this problem, we propose a simple modification to the citation text generation task: the generation target is not only the citation itself, but the entire context window, including the target citation. This approach can be easily applied to any abstractive citation generation system, and our experimental results show that training in this way is preferred by human readers and allows the generation model to make use of contextual clues about what topic to discuss and what stance to take.

Contextualizing Generated Citation Texts

lrec-main.340

Poster

2402.18054

https://aclanthology.org/2024.lrec-main.341.bib

https://aclanthology.org/2024.lrec-main.341/

@inproceedings{jiang-etal-2024-contextual, title = "Contextual Modeling for Document-level {ASR} Error Correction", author = "Jiang, Jin and Yin, Xunjian and Wan, Xiaojun and Peng, Wei and Li, Rongjun and Yang, Jingyuan and Zhou, Yanquan", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.341", pages = "3855--3867", abstract = "Contextual information, including the sentences in the same document and in other documents of the dataset, plays a crucial role in improving the accuracy of document-level ASR Error Correction (AEC), while most previous works ignore this. In this paper, we propose a context-aware method that utilizes a $k$-Nearest Neighbors ($k$NN) approach to enhance the AEC model by retrieving a datastore containing contextual information. We conduct experiments on two English and two Chinese datasets, and the results demonstrate that our proposed model can effectively utilize contextual information to improve document-level AEC. Furthermore, the context information from the whole dataset provides even better results.", }

Contextual information, including the sentences in the same document and in other documents of the dataset, plays a crucial role in improving the accuracy of document-level ASR Error Correction (AEC), while most previous works ignore this. In this paper, we propose a context-aware method that utilizes a $k$-Nearest Neighbors ($k$NN) approach to enhance the AEC model by retrieving a datastore containing contextual information. We conduct experiments on two English and two Chinese datasets, and the results demonstrate that our proposed model can effectively utilize contextual information to improve document-level AEC. Furthermore, the context information from the whole dataset provides even better results.

Contextual Modeling for Document-level ASR Error Correction

lrec-main.341

Poster

https://aclanthology.org/2024.lrec-main.342.bib

https://aclanthology.org/2024.lrec-main.342/

@inproceedings{zhang-etal-2024-continual, title = "Continual Few-shot Event Detection via Hierarchical Augmentation Networks", author = "Zhang, Chenlong and Cao, Pengfei and Chen, Yubo and Liu, Kang and Zhang, Zhiqiang and Sun, Mengshu and Zhao, Jun", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.342", pages = "3868--3880", abstract = "Traditional continual event detection relies on abundant labeled data for training, which is often impractical to obtain in real-world applications. In this paper, we introduce continual few-shot event detection (CFED), a more commonly encountered scenario when a substantial number of labeled samples are not accessible. The CFED task is challenging as it involves memorizing previous event types and learning new event types with few-shot samples. To mitigate these challenges, we propose a memory-based framework: Hierarchical Augmentation Network (HANet). To memorize previous event types with limited memory, we incorporate prototypical augmentation into the memory set. For the issue of learning new event types in few-shot scenarios, we propose a contrastive augmentation module for token representations. Despite comparing with previous state-of-the-art methods, we also conduct comparisons with ChatGPT. Experiment results demonstrate that our method significantly outperforms all of these methods in multiple continual few-shot event detection tasks.", }

Traditional continual event detection relies on abundant labeled data for training, which is often impractical to obtain in real-world applications. In this paper, we introduce continual few-shot event detection (CFED), a more commonly encountered scenario when a substantial number of labeled samples are not accessible. The CFED task is challenging as it involves memorizing previous event types and learning new event types with few-shot samples. To mitigate these challenges, we propose a memory-based framework: Hierarchical Augmentation Network (HANet). To memorize previous event types with limited memory, we incorporate prototypical augmentation into the memory set. For the issue of learning new event types in few-shot scenarios, we propose a contrastive augmentation module for token representations. Despite comparing with previous state-of-the-art methods, we also conduct comparisons with ChatGPT. Experiment results demonstrate that our method significantly outperforms all of these methods in multiple continual few-shot event detection tasks.

Continual Few-shot Event Detection via Hierarchical Augmentation Networks

lrec-main.342

Poster

2403.17733

https://aclanthology.org/2024.lrec-main.343.bib

https://aclanthology.org/2024.lrec-main.343/

@inproceedings{shulev-simaan-2024-continual, title = "Continual Reinforcement Learning for Controlled Text Generation", author = "Shulev, Velizar and Sima{'}an, Khalil", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.343", pages = "3881--3889", abstract = "Controlled Text Generation (CTG) steers the generation of continuations of a given context (prompt) by a Large Language Model (LLM) towards texts possessing a given attribute (e.g., topic, sentiment). In this paper we view CTG as a Continual Learning problem: how to learn at every step to steer next-word generation, without having to wait for end-of-sentence. This continual view is useful for online applications such as CTG for speech, where end-of-sentence is often uncertain. We depart from an existing model, the Plug-and-Play language models (PPLM), which perturbs the context at each step to better predict next-words that posses the desired attribute. While PPLM is intricate and has many hyper-parameters, we provide a proof that the PPLM objective function can be reduced to a Continual Reinforcement Learning (CRL) reward function, thereby simplifying PPLM and endowing it with a better understood learning framework. Subsequently, we present, the first of its kind, CTG algorithm that is fully based on CRL and exhibit promising empirical results.", }

Controlled Text Generation (CTG) steers the generation of continuations of a given context (prompt) by a Large Language Model (LLM) towards texts possessing a given attribute (e.g., topic, sentiment). In this paper we view CTG as a Continual Learning problem: how to learn at every step to steer next-word generation, without having to wait for end-of-sentence. This continual view is useful for online applications such as CTG for speech, where end-of-sentence is often uncertain. We depart from an existing model, the Plug-and-Play language models (PPLM), which perturbs the context at each step to better predict next-words that posses the desired attribute. While PPLM is intricate and has many hyper-parameters, we provide a proof that the PPLM objective function can be reduced to a Continual Reinforcement Learning (CRL) reward function, thereby simplifying PPLM and endowing it with a better understood learning framework. Subsequently, we present, the first of its kind, CTG algorithm that is fully based on CRL and exhibit promising empirical results.

Continual Reinforcement Learning for Controlled Text Generation

lrec-main.343

Poster

https://aclanthology.org/2024.lrec-main.344.bib

https://aclanthology.org/2024.lrec-main.344/

@inproceedings{wang-etal-2024-continued, title = "Continued Pre-training on Sentence Analogies for Translation with Small Data", author = "Wang, Liyan and Wang, Haotong and Lepage, Yves", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.344", pages = "3890--3896", abstract = "This paper introduces Continued Pre-training on Analogies (CPoA) to incorporate pre-trained language models with analogical abilities, aiming at improving performance in low-resource translations without data augmentation. We continue training the models on sentence analogies retrieved from a translation corpus. Considering the sparsity of analogy in corpora, especially in low-resource scenarios, we propose exploring approximate analogies between sentences. We attempt to find sentence analogies that might not conform to formal criteria for entire sentences but partial pieces. When training the models, we introduce a weighting scalar pertaining to the quality of analogies to adjust the influence: emphasizing closer analogies while diminishing the impact of far ones. We evaluate our approach on a low-resource translation task: German-Upper Sorbian. The results show that CPoA using 10 times fewer instances can effectively attain gains of +1.4 and +1.3 BLEU points over the original model in two translation directions. This improvement is more pronounced when there are fewer parallel examples.", }

This paper introduces Continued Pre-training on Analogies (CPoA) to incorporate pre-trained language models with analogical abilities, aiming at improving performance in low-resource translations without data augmentation. We continue training the models on sentence analogies retrieved from a translation corpus. Considering the sparsity of analogy in corpora, especially in low-resource scenarios, we propose exploring approximate analogies between sentences. We attempt to find sentence analogies that might not conform to formal criteria for entire sentences but partial pieces. When training the models, we introduce a weighting scalar pertaining to the quality of analogies to adjust the influence: emphasizing closer analogies while diminishing the impact of far ones. We evaluate our approach on a low-resource translation task: German-Upper Sorbian. The results show that CPoA using 10 times fewer instances can effectively attain gains of +1.4 and +1.3 BLEU points over the original model in two translation directions. This improvement is more pronounced when there are fewer parallel examples.

Continued Pre-training on Sentence Analogies for Translation with Small Data

lrec-main.344

Poster

https://aclanthology.org/2024.lrec-main.345.bib

https://aclanthology.org/2024.lrec-main.345/

@inproceedings{wu-etal-2024-continuous, title = "Continuous Relational Diffusion Driven Topic Model with Multi-grained Text for Microblog", author = "Wu, Chenhao and He, Ruifang and Liu, Chang and Wang, Bo", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.345", pages = "3897--3906", abstract = "Topic model is a statistical model that leverages unsupervised learning to mine hidden topics in document collections. The data sparsity and colloquialism of social texts make it difficult to accurately mine the topics. Traditional methods assume that there are only 0/1-state relationships between the two parties in the social networks, but the relationship status in real life is more complicated, such as continuously changing relationships with different degrees of intimacy. This paper proposes a continuous relational diffusion driven topic model (CRTM) with multi-grained text for microblog to realize the continuous representation of the relationship state and make up for the context and structural information lost by previous representation methods. Multi-grained text representation learning distinguishes the impact of formal and informal expression on the topics further and alleviates colloquialism problems. Specifically, based on the original social network, the reconstructed social network with continuous relationship status is obtained by using information diffusion technology. The graph convolution model is utilized to learn node embeddings through the new social network. Finally, the neural variational inference is applied to generate topics according to continuous relationships. We validate CRTM on three real datasets, and the experimental results show the effectiveness of the scheme.", }

Topic model is a statistical model that leverages unsupervised learning to mine hidden topics in document collections. The data sparsity and colloquialism of social texts make it difficult to accurately mine the topics. Traditional methods assume that there are only 0/1-state relationships between the two parties in the social networks, but the relationship status in real life is more complicated, such as continuously changing relationships with different degrees of intimacy. This paper proposes a continuous relational diffusion driven topic model (CRTM) with multi-grained text for microblog to realize the continuous representation of the relationship state and make up for the context and structural information lost by previous representation methods. Multi-grained text representation learning distinguishes the impact of formal and informal expression on the topics further and alleviates colloquialism problems. Specifically, based on the original social network, the reconstructed social network with continuous relationship status is obtained by using information diffusion technology. The graph convolution model is utilized to learn node embeddings through the new social network. Finally, the neural variational inference is applied to generate topics according to continuous relationships. We validate CRTM on three real datasets, and the experimental results show the effectiveness of the scheme.

Continuous Relational Diffusion Driven Topic Model with Multi-grained Text for Microblog

lrec-main.345

Poster

https://aclanthology.org/2024.lrec-main.346.bib

https://aclanthology.org/2024.lrec-main.346/

@inproceedings{elzohbi-zhao-2024-contrastwsd, title = "{C}ontrast{WSD}: Enhancing Metaphor Detection with Word Sense Disambiguation Following the Metaphor Identification Procedure", author = "Elzohbi, Mohamad and Zhao, Richard", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.346", pages = "3907--3915", abstract = "This paper presents ContrastWSD, a RoBERTa-based metaphor detection model that integrates the Metaphor Identification Procedure (MIP) and Word Sense Disambiguation (WSD) to extract and contrast the contextual meaning with the basic meaning of a word to determine whether it is used metaphorically in a sentence. By utilizing the word senses derived from a WSD model, our model enhances the metaphor detection process and outperforms other methods that rely solely on contextual embeddings or integrate only the basic definitions and other external knowledge. We evaluate our approach on various benchmark datasets and compare it with strong baselines, indicating the effectiveness in advancing metaphor detection.", }

This paper presents ContrastWSD, a RoBERTa-based metaphor detection model that integrates the Metaphor Identification Procedure (MIP) and Word Sense Disambiguation (WSD) to extract and contrast the contextual meaning with the basic meaning of a word to determine whether it is used metaphorically in a sentence. By utilizing the word senses derived from a WSD model, our model enhances the metaphor detection process and outperforms other methods that rely solely on contextual embeddings or integrate only the basic definitions and other external knowledge. We evaluate our approach on various benchmark datasets and compare it with strong baselines, indicating the effectiveness in advancing metaphor detection.

ContrastWSD: Enhancing Metaphor Detection with Word Sense Disambiguation Following the Metaphor Identification Procedure

lrec-main.346

Poster

2309.03103

https://aclanthology.org/2024.lrec-main.347.bib

https://aclanthology.org/2024.lrec-main.347/

@inproceedings{cardon-etal-2024-contribution, title = "Contribution of Move Structure to Automatic Genre Identification: An Annotated Corpus of {F}rench Tourism Websites", author = "Cardon, R{\'e}mi and Pham, Trang Tran Hanh and Zakhia Doueihi, Julien and Fran{\c{c}}ois, Thomas", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.347", pages = "3916--3926", abstract = "The present work studies the contribution of move structure to automatic genre identification. This concept - well known in other branches of genre analysis - seems to have little application in natural language processing. We describe how we collect a corpus of websites in French related to tourism and annotate it with move structure. We conduct experiments on automatic genre identification with our corpus. Our results show that our approach for informing a model with move structure can increase its performance for automatic genre identification, and reduce the need for annotated data and computational power.", }

The present work studies the contribution of move structure to automatic genre identification. This concept - well known in other branches of genre analysis - seems to have little application in natural language processing. We describe how we collect a corpus of websites in French related to tourism and annotate it with move structure. We conduct experiments on automatic genre identification with our corpus. Our results show that our approach for informing a model with move structure can increase its performance for automatic genre identification, and reduce the need for annotated data and computational power.

Contribution of Move Structure to Automatic Genre Identification: An Annotated Corpus of French Tourism Websites

lrec-main.347

Poster

https://aclanthology.org/2024.lrec-main.348.bib

https://aclanthology.org/2024.lrec-main.348/

@inproceedings{ogasa-etal-2024-controllable, title = "Controllable Paraphrase Generation for Semantic and Lexical Similarities", author = "Ogasa, Yuya and Kajiwara, Tomoyuki and Arase, Yuki", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.348", pages = "3927--3942", abstract = "We developed a controllable paraphrase generation model for semantic and lexical similarities using a simple and intuitive mechanism: attaching tags to specify these values at the head of the input sentence. Lexically diverse paraphrases have been long coveted for data augmentation. However, their generation is not straightforward because diversifying surfaces easily degrades semantic similarity. Furthermore, our experiments revealed two critical features in data augmentation by paraphrasing: appropriate similarities of paraphrases are highly downstream task-dependent, and mixing paraphrases of various similarities negatively affects the downstream tasks. These features indicated that the controllability in paraphrase generation is crucial for successful data augmentation. We tackled these challenges by fine-tuning a pre-trained sequence-to-sequence model employing tags that indicate the semantic and lexical similarities of synthetic paraphrases selected carefully based on the similarities. The resultant model could paraphrase an input sentence according to the tags specified. Extensive experiments on data augmentation for contrastive learning and pre-fine-tuning of pretrained masked language models confirmed the effectiveness of the proposed model. We release our paraphrase generation model and a corpus of 87 million diverse paraphrases. (https://github.com/Ogamon958/ConPGS)", }

We developed a controllable paraphrase generation model for semantic and lexical similarities using a simple and intuitive mechanism: attaching tags to specify these values at the head of the input sentence. Lexically diverse paraphrases have been long coveted for data augmentation. However, their generation is not straightforward because diversifying surfaces easily degrades semantic similarity. Furthermore, our experiments revealed two critical features in data augmentation by paraphrasing: appropriate similarities of paraphrases are highly downstream task-dependent, and mixing paraphrases of various similarities negatively affects the downstream tasks. These features indicated that the controllability in paraphrase generation is crucial for successful data augmentation. We tackled these challenges by fine-tuning a pre-trained sequence-to-sequence model employing tags that indicate the semantic and lexical similarities of synthetic paraphrases selected carefully based on the similarities. The resultant model could paraphrase an input sentence according to the tags specified. Extensive experiments on data augmentation for contrastive learning and pre-fine-tuning of pretrained masked language models confirmed the effectiveness of the proposed model. We release our paraphrase generation model and a corpus of 87 million diverse paraphrases. (https://github.com/Ogamon958/ConPGS)

Controllable Paraphrase Generation for Semantic and Lexical Similarities

lrec-main.348

Poster

https://aclanthology.org/2024.lrec-main.349.bib

https://aclanthology.org/2024.lrec-main.349/

@inproceedings{monsen-jonsson-2024-controllable, title = "Controllable Sentence Simplification in {S}wedish Using Control Prefixes and Mined Paraphrases", author = "Monsen, Julius and Jonsson, Arne", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.349", pages = "3943--3954", abstract = "Making information accessible to diverse target audiences, including individuals with dyslexia and cognitive disabilities, is crucial. Automatic Text Simplification (ATS) systems aim to facilitate readability and comprehension by reducing linguistic complexity. However, they often lack customizability to specific user needs, and training data for smaller languages can be scarce. This paper addresses ATS in a Swedish context, using methods that provide more control over the simplification. A dataset of Swedish paraphrases is mined from large amounts of text and used to train ATS models utilizing prefix-tuning with control prefixes. We also introduce a novel data-driven method for selecting complexity attributes for controlling the simplification and compare it with previous approaches. Evaluation of the trained models using SARI and BLEU demonstrates significant improvements over the baseline {---} a fine-tuned Swedish BART model {---} and compared to previous Swedish ATS results. These findings highlight the effectiveness of employing paraphrase data in conjunction with controllable generation mechanisms for simplification. Additionally, the set of explored attributes yields similar results compared to previously used attributes, indicating their ability to capture important simplification aspects.", }

Making information accessible to diverse target audiences, including individuals with dyslexia and cognitive disabilities, is crucial. Automatic Text Simplification (ATS) systems aim to facilitate readability and comprehension by reducing linguistic complexity. However, they often lack customizability to specific user needs, and training data for smaller languages can be scarce. This paper addresses ATS in a Swedish context, using methods that provide more control over the simplification. A dataset of Swedish paraphrases is mined from large amounts of text and used to train ATS models utilizing prefix-tuning with control prefixes. We also introduce a novel data-driven method for selecting complexity attributes for controlling the simplification and compare it with previous approaches. Evaluation of the trained models using SARI and BLEU demonstrates significant improvements over the baseline {---} a fine-tuned Swedish BART model {---} and compared to previous Swedish ATS results. These findings highlight the effectiveness of employing paraphrase data in conjunction with controllable generation mechanisms for simplification. Additionally, the set of explored attributes yields similar results compared to previously used attributes, indicating their ability to capture important simplification aspects.

Controllable Sentence Simplification in Swedish Using Control Prefixes and Mined Paraphrases

lrec-main.349

Poster

https://aclanthology.org/2024.lrec-main.350.bib

https://aclanthology.org/2024.lrec-main.350/

@inproceedings{kaneko-okazaki-2024-controlled, title = "Controlled Generation with Prompt Insertion for Natural Language Explanations in Grammatical Error Correction", author = "Kaneko, Masahiro and Okazaki, Naoaki", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.350", pages = "3955--3961", abstract = "In Grammatical Error Correction (GEC), it is crucial to ensure the user{'}s comprehension of a reason for correction. Existing studies present tokens, examples, and hints for corrections, but do not directly explain the reasons in natural language. Although methods that use Large Language Models (LLMs) to provide direct explanations in natural language have been proposed for various tasks, no such method exists for GEC. Generating explanations for GEC corrections involves aligning input and output tokens, identifying correction points, and presenting corresponding explanations consistently. However, it is not straightforward to specify a complex format to generate explanations, because explicit control of generation is difficult with prompts. This study introduces a method called controlled generation with Prompt Insertion (PI) so that LLMs can explain the reasons for corrections in natural language. In PI, LLMs first correct the input text, and then we automatically extract the correction points based on the rules. The extracted correction points are sequentially inserted into the LLM{'}s explanation output as prompts, guiding the LLMs to generate explanations for the correction points. We also create an Explainable GEC (XGEC) dataset of correction reasons by annotating NUCLE, CoNLL2013, and CoNLL2014. Although generations from GPT-3.5 and ChatGPT using original prompts miss some correction points, the generation control using PI can explicitly guide to describe explanations for all correction points, contributing to improved performance in generating correction reasons.", }

In Grammatical Error Correction (GEC), it is crucial to ensure the user{'}s comprehension of a reason for correction. Existing studies present tokens, examples, and hints for corrections, but do not directly explain the reasons in natural language. Although methods that use Large Language Models (LLMs) to provide direct explanations in natural language have been proposed for various tasks, no such method exists for GEC. Generating explanations for GEC corrections involves aligning input and output tokens, identifying correction points, and presenting corresponding explanations consistently. However, it is not straightforward to specify a complex format to generate explanations, because explicit control of generation is difficult with prompts. This study introduces a method called controlled generation with Prompt Insertion (PI) so that LLMs can explain the reasons for corrections in natural language. In PI, LLMs first correct the input text, and then we automatically extract the correction points based on the rules. The extracted correction points are sequentially inserted into the LLM{'}s explanation output as prompts, guiding the LLMs to generate explanations for the correction points. We also create an Explainable GEC (XGEC) dataset of correction reasons by annotating NUCLE, CoNLL2013, and CoNLL2014. Although generations from GPT-3.5 and ChatGPT using original prompts miss some correction points, the generation control using PI can explicitly guide to describe explanations for all correction points, contributing to improved performance in generating correction reasons.

Controlled Generation with Prompt Insertion for Natural Language Explanations in Grammatical Error Correction

lrec-main.350

Poster

2309.11439

https://huggingface.co/papers/2309.11439

https://aclanthology.org/2024.lrec-main.351.bib

https://aclanthology.org/2024.lrec-main.351/

@inproceedings{wang-etal-2024-controversialqa, title = "{C}ontroversial{QA}: Exploring Controversy in Question Answering", author = "Wang, Zhen and Zhu, Peide and Yang, Jie", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.351", pages = "3962--3966", abstract = "Controversy is widespread online. Previous studies mainly define controversy based on vague assumptions of its relation to sentiment such as hate speech and offensive words. This paper introduces the first question-answering dataset that defines content controversy by user perception, i.e., votes from plenty of users. It contains nearly 10K questions, and each question has a best answer and a most controversial answer. Experimental results reveal that controversy detection in question answering is essential and challenging, and there is no strong correlation between controversy and sentiment tasks. We also show that controversial answers and most acceptable answers cannot be distinguished by retrieval-based QA models, which may cause controversy issues. With these insights, we believe ControversialQA can inspire future research on controversy in QA systems.", }

Controversy is widespread online. Previous studies mainly define controversy based on vague assumptions of its relation to sentiment such as hate speech and offensive words. This paper introduces the first question-answering dataset that defines content controversy by user perception, i.e., votes from plenty of users. It contains nearly 10K questions, and each question has a best answer and a most controversial answer. Experimental results reveal that controversy detection in question answering is essential and challenging, and there is no strong correlation between controversy and sentiment tasks. We also show that controversial answers and most acceptable answers cannot be distinguished by retrieval-based QA models, which may cause controversy issues. With these insights, we believe ControversialQA can inspire future research on controversy in QA systems.

ControversialQA: Exploring Controversy in Question Answering

lrec-main.351

Poster

2302.05061

https://aclanthology.org/2024.lrec-main.352.bib

https://aclanthology.org/2024.lrec-main.352/

@inproceedings{mohapatra-etal-2024-conversational, title = "Conversational Grounding: Annotation and Analysis of Grounding Acts and Grounding Units", author = "Mohapatra, Biswesh and Hassan, Seemab and Romary, Laurent and Cassell, Justine", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.352", pages = "3967--3977", abstract = "Successful conversations often rest on common understanding, where all parties are on the same page about the information being shared. This process, known as conversational grounding, is crucial for building trustworthy dialog systems that can accurately keep track of and recall the shared information. The proficiencies of an agent in grounding the conveyed information significantly contribute to building a reliable dialog system. Despite recent advancements in dialog systems, there exists a noticeable deficit in their grounding capabilities. Traum (Traum, 1995) provided a framework for conversational grounding introducing Grounding Acts and Grounding Units, but substantial progress, especially in the realm of Large Language Models, remains lacking. To bridge this gap, we present the annotation of two dialog corpora employing Grounding Acts, Grounding Units, and a measure of their degree of grounding. We discuss our key findings during the annotation and also provide a baseline model to test the performance of current Language Models in categorizing the grounding acts of the dialogs. Our work aims to provide a useful resource for further research in making conversations with machines better understood and more reliable in natural day-to-day collaborative dialogs.", }

Successful conversations often rest on common understanding, where all parties are on the same page about the information being shared. This process, known as conversational grounding, is crucial for building trustworthy dialog systems that can accurately keep track of and recall the shared information. The proficiencies of an agent in grounding the conveyed information significantly contribute to building a reliable dialog system. Despite recent advancements in dialog systems, there exists a noticeable deficit in their grounding capabilities. Traum (Traum, 1995) provided a framework for conversational grounding introducing Grounding Acts and Grounding Units, but substantial progress, especially in the realm of Large Language Models, remains lacking. To bridge this gap, we present the annotation of two dialog corpora employing Grounding Acts, Grounding Units, and a measure of their degree of grounding. We discuss our key findings during the annotation and also provide a baseline model to test the performance of current Language Models in categorizing the grounding acts of the dialogs. Our work aims to provide a useful resource for further research in making conversations with machines better understood and more reliable in natural day-to-day collaborative dialogs.

Conversational Grounding: Annotation and Analysis of Grounding Acts and Grounding Units

lrec-main.352

Poster

2403.16609

https://aclanthology.org/2024.lrec-main.353.bib

https://aclanthology.org/2024.lrec-main.353/

@inproceedings{forkel-etal-2024-converting, title = "Converting Legacy Data to {CLDF}: A {FAIR} Exit Strategy for Linguistic Web Apps", author = "Forkel, Robert and Swanson, Daniel G. and Moran, Steven", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.353", pages = "3978--3982", abstract = "In the mid 2000s, there were several large-scale US National Science Foundation (NSF) grants awarded to projects aiming at developing digital infrastructure and standards for different forms of linguistics data. For example, MultiTree encoded language family trees as phylogenies in XML and LL-MAP converted detailed geographic maps of endangered languages into KML. As early stand-alone website applications, these projects allowed researchers interested in comparative linguistics to explore language genealogies and areality, respectively. However as time passed, the technologies that supported these web apps became deprecated, unsupported, and inaccessible. Here we take a future-oriented approach to digital obsolescence and illustrate how to convert legacy linguistic resources into FAIR data via the Cross-Linguistic Data Formats (CLDF). CLDF is built on the W3C recommendations Model for Tabular Data and Metadata on the Web and Metadata Vocabulary for Tabular Data developed by the CSVW (CSV on the Web) working group. Thus, each dataset is modeled as a set of tabular data files described by metadata in JSON. These standards and the tools built to validate and manipulate them provide an accessible and extensible format for converting legacy linguistic web apps into FAIR datasets.", }

In the mid 2000s, there were several large-scale US National Science Foundation (NSF) grants awarded to projects aiming at developing digital infrastructure and standards for different forms of linguistics data. For example, MultiTree encoded language family trees as phylogenies in XML and LL-MAP converted detailed geographic maps of endangered languages into KML. As early stand-alone website applications, these projects allowed researchers interested in comparative linguistics to explore language genealogies and areality, respectively. However as time passed, the technologies that supported these web apps became deprecated, unsupported, and inaccessible. Here we take a future-oriented approach to digital obsolescence and illustrate how to convert legacy linguistic resources into FAIR data via the Cross-Linguistic Data Formats (CLDF). CLDF is built on the W3C recommendations Model for Tabular Data and Metadata on the Web and Metadata Vocabulary for Tabular Data developed by the CSVW (CSV on the Web) working group. Thus, each dataset is modeled as a set of tabular data files described by metadata in JSON. These standards and the tools built to validate and manipulate them provide an accessible and extensible format for converting legacy linguistic web apps into FAIR datasets.

Converting Legacy Data to CLDF: A FAIR Exit Strategy for Linguistic Web Apps

lrec-main.353

Poster

https://aclanthology.org/2024.lrec-main.354.bib

https://aclanthology.org/2024.lrec-main.354/

@inproceedings{choi-etal-2024-cookingsense, title = "{C}ooking{S}ense: A Culinary Knowledgebase with Multidisciplinary Assertions", author = "Choi, Donghee and Gim, Mogan and Park, Donghyeon and Sung, Mujeen and Kim, Hyunjae and Kang, Jaewoo and Choi, Jihun", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.354", pages = "3983--3996", abstract = "This paper introduces CookingSense, a descriptive collection of knowledge assertions in the culinary domain extracted from various sources, including web data, scientific papers, and recipes, from which knowledge covering a broad range of aspects is acquired. CookingSense is constructed through a series of dictionary-based filtering and language model-based semantic filtering techniques, which results in a rich knowledgebase of multidisciplinary food-related assertions. Additionally, we present FoodBench, a novel benchmark to evaluate culinary decision support systems. From evaluations with FoodBench, we empirically prove that CookingSense improves the performance of retrieval augmented language models. We also validate the quality and variety of assertions in CookingSense through qualitative analysis.", }

This paper introduces CookingSense, a descriptive collection of knowledge assertions in the culinary domain extracted from various sources, including web data, scientific papers, and recipes, from which knowledge covering a broad range of aspects is acquired. CookingSense is constructed through a series of dictionary-based filtering and language model-based semantic filtering techniques, which results in a rich knowledgebase of multidisciplinary food-related assertions. Additionally, we present FoodBench, a novel benchmark to evaluate culinary decision support systems. From evaluations with FoodBench, we empirically prove that CookingSense improves the performance of retrieval augmented language models. We also validate the quality and variety of assertions in CookingSense through qualitative analysis.

CookingSense: A Culinary Knowledgebase with Multidisciplinary Assertions

lrec-main.354

Poster

2405.00523

https://aclanthology.org/2024.lrec-main.355.bib

https://aclanthology.org/2024.lrec-main.355/

@inproceedings{luo-etal-2024-corelation, title = "{C}o{R}elation: Boosting Automatic {ICD} Coding through Contextualized Code Relation Learning", author = "Luo, Junyu and Wang, Xiaochen and Wang, Jiaqi and Chang, Aofei and Wang, Yaqing and Ma, Fenglong", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.355", pages = "3997--4007", abstract = "Automatic International Classification of Diseases (ICD) coding plays a crucial role in the extraction of relevant information from clinical notes for proper recording and billing. One of the most important directions for boosting the performance of automatic ICD coding is modeling ICD code relations. However, current methods insufficiently model the intricate relationships among ICD codes and often overlook the importance of context in clinical notes. In this paper, we propose a novel approach, a contextualized and flexible framework, to enhance the learning of ICD code representations. Our approach, unlike existing methods, employs a dependent learning paradigm that considers the context of clinical notes in modeling all possible code relations. We evaluate our approach on six public ICD coding datasets and the experimental results demonstrate the effectiveness of our approach compared to state-of-the-art baselines.", }

Automatic International Classification of Diseases (ICD) coding plays a crucial role in the extraction of relevant information from clinical notes for proper recording and billing. One of the most important directions for boosting the performance of automatic ICD coding is modeling ICD code relations. However, current methods insufficiently model the intricate relationships among ICD codes and often overlook the importance of context in clinical notes. In this paper, we propose a novel approach, a contextualized and flexible framework, to enhance the learning of ICD code representations. Our approach, unlike existing methods, employs a dependent learning paradigm that considers the context of clinical notes in modeling all possible code relations. We evaluate our approach on six public ICD coding datasets and the experimental results demonstrate the effectiveness of our approach compared to state-of-the-art baselines.

CoRelation: Boosting Automatic ICD Coding through Contextualized Code Relation Learning

lrec-main.355

Poster

2402.15700

https://aclanthology.org/2024.lrec-main.356.bib

https://aclanthology.org/2024.lrec-main.356/

@inproceedings{nguyen-etal-2024-cori, title = "{CORI}: {CJKV} Benchmark with {R}omanization Integration - a Step towards Cross-lingual Transfer beyond Textual Scripts", author = "Nguyen, Hoang and Zhang, Chenwei and Liu, Ye and Parde, Natalie and Rohrbaugh, Eugene and Yu, Philip S.", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.356", pages = "4008--4020", abstract = "Naively assuming English as a source language may hinder cross-lingual transfer for many languages by failing to consider the importance of language contact. Some languages are more well-connected than others, and target languages can benefit from transferring from closely related languages; for many languages, the set of closely related languages does not include English. In this work, we study the impact of source language for cross-lingual transfer, demonstrating the importance of selecting source languages that have high contact with the target language. We also construct a novel benchmark dataset for close contact Chinese-Japanese-Korean-Vietnamese (CJKV) languages to further encourage in-depth studies of language contact. To comprehensively capture contact between these languages, we propose to integrate Romanized transcription beyond textual scripts via Contrastive Learning objectives, leading to enhanced cross-lingual representations and effective zero-shot cross-lingual transfer.", }

Naively assuming English as a source language may hinder cross-lingual transfer for many languages by failing to consider the importance of language contact. Some languages are more well-connected than others, and target languages can benefit from transferring from closely related languages; for many languages, the set of closely related languages does not include English. In this work, we study the impact of source language for cross-lingual transfer, demonstrating the importance of selecting source languages that have high contact with the target language. We also construct a novel benchmark dataset for close contact Chinese-Japanese-Korean-Vietnamese (CJKV) languages to further encourage in-depth studies of language contact. To comprehensively capture contact between these languages, we propose to integrate Romanized transcription beyond textual scripts via Contrastive Learning objectives, leading to enhanced cross-lingual representations and effective zero-shot cross-lingual transfer.

CORI: CJKV Benchmark with Romanization Integration - a Step towards Cross-lingual Transfer beyond Textual Scripts

lrec-main.356

Poster

https://aclanthology.org/2024.lrec-main.357.bib

https://aclanthology.org/2024.lrec-main.357/

@inproceedings{bentum-etal-2024-corpus, title = "Corpus Creation and Automatic Alignment of Historical {D}utch Dialect Speech", author = "Bentum, Martijn and Sanders, Eric and van den Bosch, Antal P.J. and Zeldenrust, Douwe and van den Heuvel, Henk", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.357", pages = "4021--4029", abstract = "The Dutch Dialect Database (also known as the {`}Nederlandse Dialectenbank{'}) contains dialectal variations of Dutch that were recorded all over the Netherlands in the second half of the twentieth century. A subset of these recordings of about 300 hours were enriched with manual orthographic transcriptions, using non-standard approximations of dialectal speech. In this paper we describe the creation of a corpus containing both the audio recordings and their corresponding transcriptions and focus on our method for aligning the recordings with the transcriptions and the metadata.", }

The Dutch Dialect Database (also known as the {`}Nederlandse Dialectenbank{'}) contains dialectal variations of Dutch that were recorded all over the Netherlands in the second half of the twentieth century. A subset of these recordings of about 300 hours were enriched with manual orthographic transcriptions, using non-standard approximations of dialectal speech. In this paper we describe the creation of a corpus containing both the audio recordings and their corresponding transcriptions and focus on our method for aligning the recordings with the transcriptions and the metadata.

Corpus Creation and Automatic Alignment of Historical Dutch Dialect Speech

lrec-main.357

Poster

https://aclanthology.org/2024.lrec-main.358.bib

https://aclanthology.org/2024.lrec-main.358/

@inproceedings{riaposov-lazarenko-2024-corpus, title = "Corpus Services: A Framework to Curate {XML} Corpus Data", author = "Riaposov, Aleksandr and Lazarenko, Elena", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.358", pages = "4030--4035", abstract = "This paper provides a comprehensive description of the Corpus Services framework{---}a collection of Java validation tools for language corpora compiled in XML-based data formats, in particular those using EXMARaLDA corpus software. Having successfully found application in several research projects, the core functionality of the framework is currently integrated in the automated curation and publication workflows for EXMARaLDA-driven corpora of Northern Eurasian languages, as developed by the long-term project INEL. Preliminary stages of development and examples of practical use cases are covered, a structured explanation of the framework{'}s current functionality and operational mechanisms is provided. Furthermore, the utilization of Corpus Services is extensively illustrated within the context of INEL workflows.", }

This paper provides a comprehensive description of the Corpus Services framework{---}a collection of Java validation tools for language corpora compiled in XML-based data formats, in particular those using EXMARaLDA corpus software. Having successfully found application in several research projects, the core functionality of the framework is currently integrated in the automated curation and publication workflows for EXMARaLDA-driven corpora of Northern Eurasian languages, as developed by the long-term project INEL. Preliminary stages of development and examples of practical use cases are covered, a structured explanation of the framework{'}s current functionality and operational mechanisms is provided. Furthermore, the utilization of Corpus Services is extensively illustrated within the context of INEL workflows.

Corpus Services: A Framework to Curate XML Corpus Data

lrec-main.358

Poster

https://aclanthology.org/2024.lrec-main.359.bib

https://aclanthology.org/2024.lrec-main.359/

@inproceedings{zhao-etal-2024-correcting, title = "Correcting Language Model Bias for Text Classification in True Zero-Shot Learning", author = "Zhao, Feng and Xianlin, Wan and Yan, Cheng and Loo, Chu Kiong", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.359", pages = "4036--4046", abstract = "Combining pre-trained language models (PLMs) and manual templates is a common practice for text classification in zero-shot scenarios. However, the effect of this approach is highly volatile, ranging from random guesses to near state-of-the-art results, depending on the quality of the manual templates. In this paper, we show that this instability stems from the fact that language models tend toward predicting certain label words of text classification, and manual templates can influence this tendency. To address this, we develop a novel pipeline for annotating and filtering a few examples from unlabeled examples. Moreover, we propose a new method to measure model bias on label words that utilizes unlabeled examples as a validation set when tuning language models. Our approach does not require any pre-labeled examples. Experimental results on six text classification tasks demonstrate that the proposed approach significantly outperforms standard prompt learning in zero-shot settings, achieving up to 19.7{\%} absolute improvement and 13.8{\%} average improvement. More surprisingly, on IMDB and SST-2, our approach even exceeds all few-shot baselines.", }

Combining pre-trained language models (PLMs) and manual templates is a common practice for text classification in zero-shot scenarios. However, the effect of this approach is highly volatile, ranging from random guesses to near state-of-the-art results, depending on the quality of the manual templates. In this paper, we show that this instability stems from the fact that language models tend toward predicting certain label words of text classification, and manual templates can influence this tendency. To address this, we develop a novel pipeline for annotating and filtering a few examples from unlabeled examples. Moreover, we propose a new method to measure model bias on label words that utilizes unlabeled examples as a validation set when tuning language models. Our approach does not require any pre-labeled examples. Experimental results on six text classification tasks demonstrate that the proposed approach significantly outperforms standard prompt learning in zero-shot settings, achieving up to 19.7{\%} absolute improvement and 13.8{\%} average improvement. More surprisingly, on IMDB and SST-2, our approach even exceeds all few-shot baselines.

Correcting Language Model Bias for Text Classification in True Zero-Shot Learning

lrec-main.359

Poster

https://aclanthology.org/2024.lrec-main.360.bib

https://aclanthology.org/2024.lrec-main.360/

@inproceedings{zhang-etal-2024-correcting, title = "Correcting Pronoun Homophones with Subtle Semantics in {C}hinese Speech Recognition", author = "Zhang, Zhaobo and Gan, Rui and Yuan, Pingpeng and Jin, Hai", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.360", pages = "4047--4058", abstract = "Speech recognition is becoming prevalent in daily life. However, due to the similar semantic context of the entities and the overlap of Chinese pronunciation, the pronoun homophone, especially {``}他/她/它 (he/she/it){''}, (their pronunciation is {``}T{\=a}{''}) is usually recognized incorrectly. It poses a challenge to automatically correct them during the post-processing of Chinese speech recognition. In this paper, we propose three models to address the common confusion issues in this domain, tailored to various application scenarios. We implement the language model, the LSTM model with semantic features, and the rule-based assisted Ngram model, enabling our models to adapt to a wide range of requirements, from high-precision to low-resource offline devices. The extensive experiments show that our models achieve the highest recognition rate for {``}T{\=a}{''} correction with improvements from 70{\%} in the popular voice input methods up to 90{\%}. Further ablation analysis underscores the effectiveness of our models in enhancing recognition accuracy. Therefore, our models improve the overall experience of Chinese speech recognition of {``}T{\=a}{''} and reduce the burden of manual transcription corrections.", }

Speech recognition is becoming prevalent in daily life. However, due to the similar semantic context of the entities and the overlap of Chinese pronunciation, the pronoun homophone, especially {``}他/她/它 (he/she/it){''}, (their pronunciation is {``}T{\=a}{''}) is usually recognized incorrectly. It poses a challenge to automatically correct them during the post-processing of Chinese speech recognition. In this paper, we propose three models to address the common confusion issues in this domain, tailored to various application scenarios. We implement the language model, the LSTM model with semantic features, and the rule-based assisted Ngram model, enabling our models to adapt to a wide range of requirements, from high-precision to low-resource offline devices. The extensive experiments show that our models achieve the highest recognition rate for {``}T{\=a}{''} correction with improvements from 70{\%} in the popular voice input methods up to 90{\%}. Further ablation analysis underscores the effectiveness of our models in enhancing recognition accuracy. Therefore, our models improve the overall experience of Chinese speech recognition of {``}T{\=a}{''} and reduce the burden of manual transcription corrections.

Correcting Pronoun Homophones with Subtle Semantics in Chinese Speech Recognition

lrec-main.360

Poster

https://aclanthology.org/2024.lrec-main.361.bib

https://aclanthology.org/2024.lrec-main.361/

@inproceedings{shah-etal-2024-correlations, title = "Correlations between Multilingual Language Model Geometry and Crosslingual Transfer Performance", author = "Shah, Cheril and Chandak, Yashashree and Mane, Atharv Mahesh and Bergen, Benjamin and Chang, Tyler A.", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.361", pages = "4059--4066", abstract = "A common approach to interpreting multilingual language models is to evaluate their internal representations. For example, studies have found that languages occupy distinct subspaces in the models{'} representation spaces, and geometric distances between languages often reflect linguistic properties such as language families and typological features. In our work, we investigate whether geometric distances between language representations correlate with zero-shot crosslingual transfer performance for POS-tagging and NER in three multilingual language models. We consider four distance metrics, including new metrics that identify a basis for a multilingual representation space that sorts axes based on their language-separability. We find that each distance metric either only moderately correlates or does not correlate with crosslingual transfer performance, and metrics do not generalize well across models, layers, and tasks. Although pairwise language separability is a reasonable predictor of crosslingual transfer, representational geometry overall is an inconsistent predictor for the crosslingual performance of multilingual language models.", }

A common approach to interpreting multilingual language models is to evaluate their internal representations. For example, studies have found that languages occupy distinct subspaces in the models{'} representation spaces, and geometric distances between languages often reflect linguistic properties such as language families and typological features. In our work, we investigate whether geometric distances between language representations correlate with zero-shot crosslingual transfer performance for POS-tagging and NER in three multilingual language models. We consider four distance metrics, including new metrics that identify a basis for a multilingual representation space that sorts axes based on their language-separability. We find that each distance metric either only moderately correlates or does not correlate with crosslingual transfer performance, and metrics do not generalize well across models, layers, and tasks. Although pairwise language separability is a reasonable predictor of crosslingual transfer, representational geometry overall is an inconsistent predictor for the crosslingual performance of multilingual language models.

Correlations between Multilingual Language Model Geometry and Crosslingual Transfer Performance

lrec-main.361

Poster

https://aclanthology.org/2024.lrec-main.362.bib

https://aclanthology.org/2024.lrec-main.362/

@inproceedings{mirovsky-etal-2024-cost, title = "Cost-Effective Discourse Annotation in the {P}rague {C}zech{--}{E}nglish {D}ependency {T}reebank", author = "M{\'\i}rovsk{\'y}, Ji{\v{r}}{\'\i} and Synkov{\'a}, Pavl{\'\i}na and Polakova, Lucie and Pacl{\'\i}kov{\'a}, Marie", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.362", pages = "4067--4077", abstract = "We present a cost-effective method for obtaining a high-quality annotation of explicit discourse relations in the Czech part of the Prague Czech{--}English Dependency Treebank, a corpus of almost 50 thousand sentences coming from the Czech translation of the Wall Street Journal part of the Penn Treebank. We use three different sources of information and combine them to obtain the discourse annotation: (i) annotation projection from the Penn Discourse Treebank 3.0, (ii) manual tectogrammatical (deep syntax) representation of sentences of the corpus, and (iii) the Lexicon of Czech Discourse Connectives CzeDLex. After solving as many discrepancies as possible automatically, the final discourse annotation is achieved by manual inspection of the remaining problematic cases. The discourse annotation of the corpus will be available both in the Prague format (on top of tectogrammatical trees) with the Prague taxonomy of discourse types, and in the Penn format (on plain texts) with the Penn Discourse Treebank 3.0 sense taxonomy.", }

We present a cost-effective method for obtaining a high-quality annotation of explicit discourse relations in the Czech part of the Prague Czech{--}English Dependency Treebank, a corpus of almost 50 thousand sentences coming from the Czech translation of the Wall Street Journal part of the Penn Treebank. We use three different sources of information and combine them to obtain the discourse annotation: (i) annotation projection from the Penn Discourse Treebank 3.0, (ii) manual tectogrammatical (deep syntax) representation of sentences of the corpus, and (iii) the Lexicon of Czech Discourse Connectives CzeDLex. After solving as many discrepancies as possible automatically, the final discourse annotation is achieved by manual inspection of the remaining problematic cases. The discourse annotation of the corpus will be available both in the Prague format (on top of tectogrammatical trees) with the Prague taxonomy of discourse types, and in the Penn format (on plain texts) with the Penn Discourse Treebank 3.0 sense taxonomy.

Cost-Effective Discourse Annotation in the Prague Czech–English Dependency Treebank

lrec-main.362

Poster

https://aclanthology.org/2024.lrec-main.363.bib

https://aclanthology.org/2024.lrec-main.363/

@inproceedings{steindl-etal-2024-counterfactual, title = "Counterfactual Dialog Mixing as Data Augmentation for Task-Oriented Dialog Systems", author = {Steindl, Sebastian and Sch{\"a}fer, Ulrich and Ludwig, Bernd}, editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.363", pages = "4078--4087", abstract = "High-quality training data for Task-Oriented Dialog (TOD) systems is costly to come by if no corpora are available. One method to extend available data is data augmentation. Yet, the research into and adaptation of data augmentation techniques for TOD systems is limited in comparison with other data modalities. We propose a novel, causally-flavored data augmentation technique called Counterfactual Dialog Mixing (CDM) that generates realistic synthetic dialogs via counterfactuals to increase the amount of training data. We demonstrate the method on a benchmark dataset and show that a model trained to classify the counterfactuals from the original data fails to do so, which strengthens the claim of creating realistic synthetic dialogs. To evaluate the effectiveness of CDM, we train a current architecture on a benchmark dataset and compare the performance with and without CDM. By doing so, we achieve state-of-the-art on some metrics. We further investigate the external generalizability and a lower resource setting. To evaluate the models, we adopted an interactive evaluation scheme.", }

High-quality training data for Task-Oriented Dialog (TOD) systems is costly to come by if no corpora are available. One method to extend available data is data augmentation. Yet, the research into and adaptation of data augmentation techniques for TOD systems is limited in comparison with other data modalities. We propose a novel, causally-flavored data augmentation technique called Counterfactual Dialog Mixing (CDM) that generates realistic synthetic dialogs via counterfactuals to increase the amount of training data. We demonstrate the method on a benchmark dataset and show that a model trained to classify the counterfactuals from the original data fails to do so, which strengthens the claim of creating realistic synthetic dialogs. To evaluate the effectiveness of CDM, we train a current architecture on a benchmark dataset and compare the performance with and without CDM. By doing so, we achieve state-of-the-art on some metrics. We further investigate the external generalizability and a lower resource setting. To evaluate the models, we adopted an interactive evaluation scheme.

Counterfactual Dialog Mixing as Data Augmentation for Task-Oriented Dialog Systems

lrec-main.363

Poster

https://aclanthology.org/2024.lrec-main.364.bib

https://aclanthology.org/2024.lrec-main.364/

@inproceedings{vazquez-2024-creating, title = "Creating Terminological Resources in the Digital Age for Less-resourced Languages", author = "V{\`a}zquez, Merc{\`e}", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.364", pages = "4088--4091", abstract = "Multilingual terminological resources contain the most representative knowledge of specialized domains and allow professionals to create and translate specialized content in order to spread knowledge. Today, representative and useful multilingual terminological resources are available for the most resourced languages. This reduces or limits the development of knowledge in less-resourced languages across different specialized domains, mainly those that are constantly evolving and creating or adapting new concepts as needed. In this paper we present our methodology for carrying out terminological projects in Catalan, based entirely on open access linguistic resources and using natural language processing tools. The main objective of this research is to maximize the Catalan terminology currently available in open access, using a combination of natural language processing tools. The results are supervised by linguists and terminologist experts before being publicly available to the public. The findings of our research provide a new approach to terminology work, making it possible to design high-volume multilingual terminological projects that are manually revised by linguists and terminologists in the context of less-resourced languages.", }

Multilingual terminological resources contain the most representative knowledge of specialized domains and allow professionals to create and translate specialized content in order to spread knowledge. Today, representative and useful multilingual terminological resources are available for the most resourced languages. This reduces or limits the development of knowledge in less-resourced languages across different specialized domains, mainly those that are constantly evolving and creating or adapting new concepts as needed. In this paper we present our methodology for carrying out terminological projects in Catalan, based entirely on open access linguistic resources and using natural language processing tools. The main objective of this research is to maximize the Catalan terminology currently available in open access, using a combination of natural language processing tools. The results are supervised by linguists and terminologist experts before being publicly available to the public. The findings of our research provide a new approach to terminology work, making it possible to design high-volume multilingual terminological projects that are manually revised by linguists and terminologists in the context of less-resourced languages.

Creating Terminological Resources in the Digital Age for Less-resourced Languages

lrec-main.364

Poster

https://aclanthology.org/2024.lrec-main.365.bib

https://aclanthology.org/2024.lrec-main.365/

@inproceedings{gupta-etal-2024-creation, title = "Creation and Analysis of an International Corpus of Privacy Laws", author = "Gupta, Sonu and Gopi, Geetika and Balaji, Harish and Poplavska, Ellen and O{'}Toole, Nora and Arora, Siddhant and Norton, Thomas and Sadeh, Norman and Wilson, Shomir", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.365", pages = "4092--4105", abstract = "The landscape of privacy laws and regulations around the world is complex and ever-changing. National and super-national laws, agreements, decrees, and other government-issued rules form a patchwork that companies must follow to operate internationally. To examine the status and evolution of this patchwork, we introduce the Privacy Law Corpus, of 1,043 privacy laws, regulations, and guidelines, covering 183 jurisdictions. This corpus enables a large-scale quantitative and qualitative examination of legal focus on privacy. We examine the temporal distribution of when privacy laws were created and illustrate the dramatic increase in privacy legislation over the past 50 years, although a finer-grained examination reveals that the rate of increase varies depending on the personal data types that privacy laws address. Our exploration also demonstrates that most privacy laws respectively address relatively few personal data types. Additionally, topic modeling results show the prevalence of common themes in privacy laws, such as finance, healthcare, and telecommunications. Finally, we release the corpus to the research community to promote further study.", }

The landscape of privacy laws and regulations around the world is complex and ever-changing. National and super-national laws, agreements, decrees, and other government-issued rules form a patchwork that companies must follow to operate internationally. To examine the status and evolution of this patchwork, we introduce the Privacy Law Corpus, of 1,043 privacy laws, regulations, and guidelines, covering 183 jurisdictions. This corpus enables a large-scale quantitative and qualitative examination of legal focus on privacy. We examine the temporal distribution of when privacy laws were created and illustrate the dramatic increase in privacy legislation over the past 50 years, although a finer-grained examination reveals that the rate of increase varies depending on the personal data types that privacy laws address. Our exploration also demonstrates that most privacy laws respectively address relatively few personal data types. Additionally, topic modeling results show the prevalence of common themes in privacy laws, such as finance, healthcare, and telecommunications. Finally, we release the corpus to the research community to promote further study.

Creation and Analysis of an International Corpus of Privacy Laws

lrec-main.365

Poster

2206.14169

https://aclanthology.org/2024.lrec-main.366.bib

https://aclanthology.org/2024.lrec-main.366/

@inproceedings{filipovic-petrovic-etal-2024-croatian, title = "{C}roatian Idioms Integration: Enhancing the {LI}dioms Multilingual Linked Idioms Dataset", author = "Filipovi{\'c} Petrovi{\'c}, Ivana and L{\'o}pez Otal, Miguel and Beliga, Slobodan", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.366", pages = "4106--4112", abstract = "Idioms, also referred to as phraseological units in some language terminologies, are a subset within the broader category of multi-word expressions. However, there is a lack of representation of idioms in Croatian, a low-resourced language, in the Linguistic Linked Open Data cloud (LLOD). To address this gap, we propose an extension of an existing RDF-based multilingual representation of idioms, referred to as the LIdioms dataset, which currently includes idioms from English, German, Italian, Portuguese, and Russian. This paper expands the existing resource by incorporating 1,042 Croatian idioms in an Ontolex Lemon format. In addition, to foster translation initiatives and facilitate intercultural exchange, these added Croatian idioms have also been linked to other idioms of the LIdioms dataset, with which they share similar meanings despite their differences in the expression aspect. This addition enriches the knowledge base of the LLOD community with a new language resource that includes Croatian idioms.", }

Idioms, also referred to as phraseological units in some language terminologies, are a subset within the broader category of multi-word expressions. However, there is a lack of representation of idioms in Croatian, a low-resourced language, in the Linguistic Linked Open Data cloud (LLOD). To address this gap, we propose an extension of an existing RDF-based multilingual representation of idioms, referred to as the LIdioms dataset, which currently includes idioms from English, German, Italian, Portuguese, and Russian. This paper expands the existing resource by incorporating 1,042 Croatian idioms in an Ontolex Lemon format. In addition, to foster translation initiatives and facilitate intercultural exchange, these added Croatian idioms have also been linked to other idioms of the LIdioms dataset, with which they share similar meanings despite their differences in the expression aspect. This addition enriches the knowledge base of the LLOD community with a new language resource that includes Croatian idioms.

Croatian Idioms Integration: Enhancing the LIdioms Multilingual Linked Idioms Dataset

lrec-main.366

Poster

https://aclanthology.org/2024.lrec-main.367.bib

https://aclanthology.org/2024.lrec-main.367/

@inproceedings{zhang-eickhoff-2024-crocosum, title = "{C}ro{C}o{S}um: A Benchmark Dataset for Cross-Lingual Code-Switched Summarization", author = "Zhang, Ruochen and Eickhoff, Carsten", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.367", pages = "4113--4126", abstract = "Cross-lingual summarization (CLS) has attracted increasing interest in recent years due to the availability of large-scale web-mined datasets and the advancements of multilingual language models. However, given the rareness of naturally occurring CLS resources, the majority of datasets are forced to rely on translation which can contain overly literal artifacts. This restricts our ability to observe naturally occurring CLS pairs that capture organic diction, including instances of code-switching. This alteration between languages in mid-message is a common phenomenon in multilingual settings yet has been largely overlooked in cross-lingual contexts due to data scarcity. To address this gap, we introduce CroCoSum, a dataset of cross-lingual code-switched summarization of technology news. It consists of over 24,000 English source articles and 18,000 human-written Chinese news summaries, with more than 92{\%} of the summaries containing code-switched phrases. For reference, we evaluate the performance of existing approaches including pipeline, end-to-end, and zero-shot methods. We show that leveraging existing CLS resources as a pretraining step does not improve performance on CroCoSum, indicating the limited generalizability of current datasets. Finally, we discuss the challenges of evaluating cross-lingual summarizers on code-switched generation through qualitative error analyses.", }

Cross-lingual summarization (CLS) has attracted increasing interest in recent years due to the availability of large-scale web-mined datasets and the advancements of multilingual language models. However, given the rareness of naturally occurring CLS resources, the majority of datasets are forced to rely on translation which can contain overly literal artifacts. This restricts our ability to observe naturally occurring CLS pairs that capture organic diction, including instances of code-switching. This alteration between languages in mid-message is a common phenomenon in multilingual settings yet has been largely overlooked in cross-lingual contexts due to data scarcity. To address this gap, we introduce CroCoSum, a dataset of cross-lingual code-switched summarization of technology news. It consists of over 24,000 English source articles and 18,000 human-written Chinese news summaries, with more than 92{\%} of the summaries containing code-switched phrases. For reference, we evaluate the performance of existing approaches including pipeline, end-to-end, and zero-shot methods. We show that leveraging existing CLS resources as a pretraining step does not improve performance on CroCoSum, indicating the limited generalizability of current datasets. Finally, we discuss the challenges of evaluating cross-lingual summarizers on code-switched generation through qualitative error analyses.

CroCoSum: A Benchmark Dataset for Cross-Lingual Code-Switched Summarization

lrec-main.367

Poster

2303.04092

https://aclanthology.org/2024.lrec-main.368.bib

https://aclanthology.org/2024.lrec-main.368/

@inproceedings{cekinel-etal-2024-cross, title = "Cross-Lingual Learning vs. Low-Resource Fine-Tuning: A Case Study with Fact-Checking in {T}urkish", author = {Cekinel, Recep Firat and {\c{C}}{\"o}ltekin, {\c{C}}a{\u{g}}r{\i} and Karagoz, Pinar}, editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.368", pages = "4127--4142", abstract = "The rapid spread of misinformation through social media platforms has raised concerns regarding its impact on public opinion. While misinformation is prevalent in other languages, the majority of research in this field has concentrated on the English language. Hence, there is a scarcity of datasets for other languages, including Turkish. To address this concern, we have introduced the FCTR dataset, consisting of 3238 real-world claims. This dataset spans multiple domains and incorporates evidence collected from three Turkish fact-checking organizations. Additionally, we aim to assess the effectiveness of cross-lingual transfer learning for low-resource languages, with a particular focus on Turkish. We demonstrate in-context learning (zero-shot and few-shot) performance of large language models in this context. The experimental results indicate that the dataset has the potential to advance research in the Turkish language.", }

The rapid spread of misinformation through social media platforms has raised concerns regarding its impact on public opinion. While misinformation is prevalent in other languages, the majority of research in this field has concentrated on the English language. Hence, there is a scarcity of datasets for other languages, including Turkish. To address this concern, we have introduced the FCTR dataset, consisting of 3238 real-world claims. This dataset spans multiple domains and incorporates evidence collected from three Turkish fact-checking organizations. Additionally, we aim to assess the effectiveness of cross-lingual transfer learning for low-resource languages, with a particular focus on Turkish. We demonstrate in-context learning (zero-shot and few-shot) performance of large language models in this context. The experimental results indicate that the dataset has the potential to advance research in the Turkish language.

Cross-Lingual Learning vs. Low-Resource Fine-Tuning: A Case Study with Fact-Checking in Turkish

lrec-main.368

Poster

2403.00411

https://aclanthology.org/2024.lrec-main.369.bib

https://aclanthology.org/2024.lrec-main.369/

@inproceedings{piskorski-etal-2024-cross, title = "Cross-lingual Named Entity Corpus for {S}lavic Languages", author = "Piskorski, Jakub and Marci{\'n}czuk, Micha{\l} and Yangarber, Roman", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.369", pages = "4143--4157", abstract = "This paper presents a corpus manually annotated with named entities for six Slavic languages {---} Bulgarian, Czech, Polish, Slovenian, Russian, and Ukrainian. This work is the result of a series of shared tasks, conducted in 2017{--}2023 as a part of the Workshops on Slavic Natural Language Processing. The corpus consists of 5,017 documents on seven topics. The documents are annotated with five classes of named entities. Each entity is described by a category, a lemma, and a unique cross-lingual identifier. We provide two train-tune dataset splits {---} single topic out and cross topics. For each split, we set benchmarks using a transformer-based neural network architecture with the pre-trained multilingual models {---} XLM-RoBERTa-large for named entity mention recognition and categorization, and mT5-large for named entity lemmatization and linking.", }

This paper presents a corpus manually annotated with named entities for six Slavic languages {---} Bulgarian, Czech, Polish, Slovenian, Russian, and Ukrainian. This work is the result of a series of shared tasks, conducted in 2017{--}2023 as a part of the Workshops on Slavic Natural Language Processing. The corpus consists of 5,017 documents on seven topics. The documents are annotated with five classes of named entities. Each entity is described by a category, a lemma, and a unique cross-lingual identifier. We provide two train-tune dataset splits {---} single topic out and cross topics. For each split, we set benchmarks using a transformer-based neural network architecture with the pre-trained multilingual models {---} XLM-RoBERTa-large for named entity mention recognition and categorization, and mT5-large for named entity lemmatization and linking.

Cross-lingual Named Entity Corpus for Slavic Languages

lrec-main.369

Poster

2404.00482

https://huggingface.co/papers/2404.00482

https://aclanthology.org/2024.lrec-main.370.bib

https://aclanthology.org/2024.lrec-main.370/

@inproceedings{tahery-etal-2024-cross, title = "Cross-Lingual {NLU}: Mitigating Language-Specific Impact in Embeddings Leveraging Adversarial Learning", author = "Tahery, Saedeh and Kianian, Sahar and Farzi, Saeed", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.370", pages = "4158--4163", abstract = "Low-resource languages and computational expenses pose significant challenges in the domain of large language models (LLMs). Currently, researchers are actively involved in various efforts to tackle these challenges. Cross-lingual natural language processing (NLP) remains one of the most promising strategies to address these issues. In this paper, we introduce a novel approach that utilizes adversarial techniques to mitigate the impact of language-specific information in contextual embeddings generated by large multilingual language models, with potential applications in cross-lingual tasks. The study encompasses five different languages, including both Latin and non-Latin ones, in the context of two fundamental tasks in natural language understanding: intent detection and slot filling. The results primarily show that our current approach excels in zero-shot scenarios for Latin languages like Spanish. However, it encounters limitations when applied to languages distant from English, such as Thai and Persian. This highlights that while our approach effectively reduces the effect of language-specific information on the core meaning, it performs better for Latin languages that share language-specific nuances with English, as certain characteristics persist in the overall meaning within embeddings.", }

Low-resource languages and computational expenses pose significant challenges in the domain of large language models (LLMs). Currently, researchers are actively involved in various efforts to tackle these challenges. Cross-lingual natural language processing (NLP) remains one of the most promising strategies to address these issues. In this paper, we introduce a novel approach that utilizes adversarial techniques to mitigate the impact of language-specific information in contextual embeddings generated by large multilingual language models, with potential applications in cross-lingual tasks. The study encompasses five different languages, including both Latin and non-Latin ones, in the context of two fundamental tasks in natural language understanding: intent detection and slot filling. The results primarily show that our current approach excels in zero-shot scenarios for Latin languages like Spanish. However, it encounters limitations when applied to languages distant from English, such as Thai and Persian. This highlights that while our approach effectively reduces the effect of language-specific information on the core meaning, it performs better for Latin languages that share language-specific nuances with English, as certain characteristics persist in the overall meaning within embeddings.

Cross-Lingual NLU: Mitigating Language-Specific Impact in Embeddings Leveraging Adversarial Learning

lrec-main.370

Poster

https://aclanthology.org/2024.lrec-main.371.bib

https://aclanthology.org/2024.lrec-main.371/

@inproceedings{hoshino-etal-2024-cross, title = "Cross-lingual Transfer or Machine Translation? On Data Augmentation for Monolingual Semantic Textual Similarity", author = "Hoshino, Sho and Kato, Akihiko and Murakami, Soichiro and Zhang, Peinan", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.371", pages = "4164--4173", abstract = "Learning better sentence embeddings leads to improved performance for natural language understanding tasks including semantic textual similarity (STS) and natural language inference (NLI). As prior studies leverage large-scale labeled NLI datasets for fine-tuning masked language models to yield sentence embeddings, task performance for languages other than English is often left behind. In this study, we directly compared two data augmentation techniques as potential solutions for monolingual STS: - (a): {\_}cross-lingual transfer{\_} that exploits English resources alone as training data to yield non-English sentence embeddings as zero-shot inference, and - (b) {\_}machine translation{\_} that coverts English data into pseudo non-English training data in advance. In our experiments on monolingual STS in Japanese and Korean, we find that the two data techniques yield performance on par. In addition, we find a superiority of Wikipedia domain over NLI domain as unlabeled training data for these languages. Combining our findings, we further demonstrate that the cross-lingual transfer of Wikipedia data exhibits improved performance.", }

Learning better sentence embeddings leads to improved performance for natural language understanding tasks including semantic textual similarity (STS) and natural language inference (NLI). As prior studies leverage large-scale labeled NLI datasets for fine-tuning masked language models to yield sentence embeddings, task performance for languages other than English is often left behind. In this study, we directly compared two data augmentation techniques as potential solutions for monolingual STS: - (a): {\_}cross-lingual transfer{\_} that exploits English resources alone as training data to yield non-English sentence embeddings as zero-shot inference, and - (b) {\_}machine translation{\_} that coverts English data into pseudo non-English training data in advance. In our experiments on monolingual STS in Japanese and Korean, we find that the two data techniques yield performance on par. In addition, we find a superiority of Wikipedia domain over NLI domain as unlabeled training data for these languages. Combining our findings, we further demonstrate that the cross-lingual transfer of Wikipedia data exhibits improved performance.

Cross-lingual Transfer or Machine Translation? On Data Augmentation for Monolingual Semantic Textual Similarity

lrec-main.371

Poster

2403.05257

https://aclanthology.org/2024.lrec-main.372.bib

https://aclanthology.org/2024.lrec-main.372/

@inproceedings{manafi-krishnaswamy-2024-cross, title = "Cross-Lingual Transfer Robustness to Lower-Resource Languages on Adversarial Datasets", author = "Manafi, Shadi and Krishnaswamy, Nikhil", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.372", pages = "4174--4184", abstract = "Multilingual Language Models (MLLMs) exhibit robust cross-lingual transfer capabilities, or the ability to leverage information acquired in a source language and apply it to a target language. These capabilities find practical applications in well-established Natural Language Processing (NLP) tasks such as Named Entity Recognition (NER). This study aims to investigate the effectiveness of a source language when applied to a target language, particularly in the context of perturbing the input test set. We evaluate on 13 pairs of languages, each including one high-resource language (HRL) and one low-resource language (LRL) with a geographic, genetic, or borrowing relationship. We evaluate two well-known MLLMs{---}MBERT and XLM-R{---}on these pairs, in native LRL and cross-lingual transfer settings, in two tasks, under a set of different perturbations. Our findings indicate that NER cross-lingual transfer depends largely on the overlap of entity chunks. If a source and target language have more entities in common, the transfer ability is stronger. Models using cross-lingual transfer also appear to be somewhat more robust to certain perturbations of the input, perhaps indicating an ability to leverage stronger representations derived from the HRL. Our research provides valuable insights into cross-lingual transfer and its implications for NLP applications, and underscores the need to consider linguistic nuances and potential limitations when employing MLLMs across distinct languages.", }

Multilingual Language Models (MLLMs) exhibit robust cross-lingual transfer capabilities, or the ability to leverage information acquired in a source language and apply it to a target language. These capabilities find practical applications in well-established Natural Language Processing (NLP) tasks such as Named Entity Recognition (NER). This study aims to investigate the effectiveness of a source language when applied to a target language, particularly in the context of perturbing the input test set. We evaluate on 13 pairs of languages, each including one high-resource language (HRL) and one low-resource language (LRL) with a geographic, genetic, or borrowing relationship. We evaluate two well-known MLLMs{---}MBERT and XLM-R{---}on these pairs, in native LRL and cross-lingual transfer settings, in two tasks, under a set of different perturbations. Our findings indicate that NER cross-lingual transfer depends largely on the overlap of entity chunks. If a source and target language have more entities in common, the transfer ability is stronger. Models using cross-lingual transfer also appear to be somewhat more robust to certain perturbations of the input, perhaps indicating an ability to leverage stronger representations derived from the HRL. Our research provides valuable insights into cross-lingual transfer and its implications for NLP applications, and underscores the need to consider linguistic nuances and potential limitations when employing MLLMs across distinct languages.

Cross-Lingual Transfer Robustness to Lower-Resource Languages on Adversarial Datasets

lrec-main.372

Poster

2403.20056

https://aclanthology.org/2024.lrec-main.373.bib

https://aclanthology.org/2024.lrec-main.373/

@inproceedings{luo-etal-2024-crosstune, title = "{C}ross{T}une: Black-Box Few-Shot Classification with Label Enhancement", author = "Luo, Danqing and Zhang, Chen and Zhang, Yan and Li, Haizhou", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.373", pages = "4185--4197", abstract = "Training or finetuning large-scale language models (LLMs) requires substantial computation resources, motivating recent efforts to explore parameter-efficient adaptation to downstream tasks. One approach is to treat these models as black boxes and use forward passes (Inference APIs) to interact with them. Current research focuses on adapting these black-box models to downstream tasks using gradient-free prompt optimization, but this often involves an expensive process of searching task-specific prompts. Therefore, we are motivated to study black-box language model adaptation without prompt search. Specifically, we introduce a label-enhanced cross-attention network called CrossTune, which models the semantic relatedness between the input text sequence and task-specific label descriptions. Its effectiveness is examined in the context of few-shot text classification. To improve the generalization of CrossTune, we utilize ChatGPT to generate additional training data through in-context learning. A switch mechanism is implemented to exclude low-quality ChatGPT-generated data. Through extensive experiments on seven benchmark text classification datasets, we demonstrate that our proposed approach outperforms the previous state-of-the-art gradient-free black-box tuning method by 5.7{\%} on average. Even without using ChatGPT-augmented data, CrossTune performs better or comparably than previous black-box tuning methods, suggesting the effectiveness of our approach.", }

Training or finetuning large-scale language models (LLMs) requires substantial computation resources, motivating recent efforts to explore parameter-efficient adaptation to downstream tasks. One approach is to treat these models as black boxes and use forward passes (Inference APIs) to interact with them. Current research focuses on adapting these black-box models to downstream tasks using gradient-free prompt optimization, but this often involves an expensive process of searching task-specific prompts. Therefore, we are motivated to study black-box language model adaptation without prompt search. Specifically, we introduce a label-enhanced cross-attention network called CrossTune, which models the semantic relatedness between the input text sequence and task-specific label descriptions. Its effectiveness is examined in the context of few-shot text classification. To improve the generalization of CrossTune, we utilize ChatGPT to generate additional training data through in-context learning. A switch mechanism is implemented to exclude low-quality ChatGPT-generated data. Through extensive experiments on seven benchmark text classification datasets, we demonstrate that our proposed approach outperforms the previous state-of-the-art gradient-free black-box tuning method by 5.7{\%} on average. Even without using ChatGPT-augmented data, CrossTune performs better or comparably than previous black-box tuning methods, suggesting the effectiveness of our approach.

CrossTune: Black-Box Few-Shot Classification with Label Enhancement

lrec-main.373

Poster

2403.12468

https://aclanthology.org/2024.lrec-main.374.bib

https://aclanthology.org/2024.lrec-main.374/

@inproceedings{bui-savary-2024-cross, title = "Cross-type {F}rench Multiword Expression Identification with Pre-trained Masked Language Models", author = "Bui, Van-Tuan and Savary, Agata", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.374", pages = "4198--4204", abstract = "Multiword expressions (MWEs) pose difficulties for natural language processing (NLP) due to their linguistic features, such as syntactic and semantic properties, which distinguish them from regular word groupings. This paper describes a combination of two systems: one that learns verbal multiword expressions (VMWEs) and another that learns non-verbal MWEs (nVMWEs). Together, these systems leverage training data from both types of MWEs to enhance performance on a cross-type dataset containing both VMWEs and nVMWEs. Such scenarios emerge when datasets are developed using differing annotation schemes. We explore the fine-tuning of several state-of-the-art neural transformers for each MWE type. Our experiments demonstrate the advantages of the combined system over multi-task approaches or single-task models, addressing the challenges posed by diverse tagsets within the training data. Specifically, we evaluated the combined system on a French treebank named Sequoia, which features an annotation layer encompassing all syntactic types of French MWEs. With this combined approach, we improved the F1-score by approximately 3{\%} on the Sequoia dataset.", }

Multiword expressions (MWEs) pose difficulties for natural language processing (NLP) due to their linguistic features, such as syntactic and semantic properties, which distinguish them from regular word groupings. This paper describes a combination of two systems: one that learns verbal multiword expressions (VMWEs) and another that learns non-verbal MWEs (nVMWEs). Together, these systems leverage training data from both types of MWEs to enhance performance on a cross-type dataset containing both VMWEs and nVMWEs. Such scenarios emerge when datasets are developed using differing annotation schemes. We explore the fine-tuning of several state-of-the-art neural transformers for each MWE type. Our experiments demonstrate the advantages of the combined system over multi-task approaches or single-task models, addressing the challenges posed by diverse tagsets within the training data. Specifically, we evaluated the combined system on a French treebank named Sequoia, which features an annotation layer encompassing all syntactic types of French MWEs. With this combined approach, we improved the F1-score by approximately 3{\%} on the Sequoia dataset.

Cross-type French Multiword Expression Identification with Pre-trained Masked Language Models

lrec-main.374

Poster

https://aclanthology.org/2024.lrec-main.375.bib

https://aclanthology.org/2024.lrec-main.375/

@inproceedings{liu-lee-2024-csswiki, title = "{CSSW}iki: A {C}hinese Sentence Simplification Dataset with Linguistic and Content Operations", author = "Liu, Fengkai and Lee, John S. Y.", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.375", pages = "4205--4213", abstract = "Sentence Simplification aims to make sentences easier to read and understand. With most effort on corpus development focused on English, the amount of annotated data is limited in Chinese. To address this need, we introduce CSSWiki, an open-source dataset for Chinese sentence simplification based on Wikipedia. This dataset contains 1.6k source sentences paired with their simplified versions. Each sentence pair is annotated with operation tags that distinguish between linguistic and content modifications. We analyze differences in annotation scheme and data statistics between CSSWiki and existing datasets. We then report baseline sentence simplification performance on CSSWiki using zero-shot and few-shot approaches with Large Language Models.", }

Sentence Simplification aims to make sentences easier to read and understand. With most effort on corpus development focused on English, the amount of annotated data is limited in Chinese. To address this need, we introduce CSSWiki, an open-source dataset for Chinese sentence simplification based on Wikipedia. This dataset contains 1.6k source sentences paired with their simplified versions. Each sentence pair is annotated with operation tags that distinguish between linguistic and content modifications. We analyze differences in annotation scheme and data statistics between CSSWiki and existing datasets. We then report baseline sentence simplification performance on CSSWiki using zero-shot and few-shot approaches with Large Language Models.

CSSWiki: A Chinese Sentence Simplification Dataset with Linguistic and Content Operations

lrec-main.375

Poster

https://aclanthology.org/2024.lrec-main.376.bib

https://aclanthology.org/2024.lrec-main.376/

@inproceedings{wang-etal-2024-ctsm, title = "{CTSM}: Combining Trait and State Emotions for Empathetic Response Model", author = "Wang, Yufeng and Chen, Chao and Yang, Zhou and Wang, Shuhui and Liao, Xiangwen", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.376", pages = "4214--4225", abstract = "Empathetic response generation endeavors to empower dialogue systems to perceive speakers{'} emotions and generate empathetic responses accordingly. Psychological research demonstrates that emotion, as an essential factor in empathy, encompasses trait emotions, which are static and context-independent, and state emotions, which are dynamic and context-dependent. However, previous studies treat them in isolation, leading to insufficient emotional perception of the context, and subsequently, less effective empathetic expression. To address this problem, we propose Combining Trait and State emotions for Empathetic Response Model (CTSM). Specifically, to sufficiently perceive emotions in dialogue, we first construct and encode trait and state emotion embeddings, and then we further enhance emotional perception capability through an emotion guidance module that guides emotion representation. In addition, we propose a cross-contrastive learning decoder to enhance the model{'}s empathetic expression capability by aligning trait and state emotions between generated responses and contexts. Both automatic and manual evaluation results demonstrate that CTSM outperforms state-of-the-art baselines and can generate more empathetic responses. Our code is available at https://github.com/wangyufeng-empty/CTSM", }

Empathetic response generation endeavors to empower dialogue systems to perceive speakers{'} emotions and generate empathetic responses accordingly. Psychological research demonstrates that emotion, as an essential factor in empathy, encompasses trait emotions, which are static and context-independent, and state emotions, which are dynamic and context-dependent. However, previous studies treat them in isolation, leading to insufficient emotional perception of the context, and subsequently, less effective empathetic expression. To address this problem, we propose Combining Trait and State emotions for Empathetic Response Model (CTSM). Specifically, to sufficiently perceive emotions in dialogue, we first construct and encode trait and state emotion embeddings, and then we further enhance emotional perception capability through an emotion guidance module that guides emotion representation. In addition, we propose a cross-contrastive learning decoder to enhance the model{'}s empathetic expression capability by aligning trait and state emotions between generated responses and contexts. Both automatic and manual evaluation results demonstrate that CTSM outperforms state-of-the-art baselines and can generate more empathetic responses. Our code is available at https://github.com/wangyufeng-empty/CTSM

CTSM: Combining Trait and State Emotions for Empathetic Response Model

lrec-main.376

Poster

2403.15516

https://aclanthology.org/2024.lrec-main.377.bib

https://aclanthology.org/2024.lrec-main.377/

@inproceedings{nguyen-etal-2024-culturax, title = "{C}ultura{X}: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages", author = "Nguyen, Thuat and Nguyen, Chien Van and Lai, Viet Dac and Man, Hieu and Ngo, Nghia Trung and Dernoncourt, Franck and Rossi, Ryan A. and Nguyen, Thien Huu", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.377", pages = "4226--4237", abstract = "Extensive training datasets represent one of the important factors for the impressive learning capabilities of large language models (LLMs). However, these training datasets for current LLMs, especially the recent state-of-the-art models, are often not fully disclosed. Creating training data for high-performing LLMs involves extensive cleaning and deduplication to ensure the necessary level of quality. The lack of transparency for training data has thus hampered research on attributing and addressing hallucination and bias issues in LLMs, hindering replication efforts and further advancements in the community. These challenges become even more pronounced in multilingual learning scenarios, where the available multilingual text datasets are often inadequately collected and cleaned. Consequently, there is a lack of open-source and readily usable dataset to effectively train LLMs in multiple languages. To overcome this issue, we present CulturaX, a substantial multilingual dataset with 6.3 trillion tokens in 167 languages, tailored for LLM development. Our dataset undergoes meticulous cleaning and deduplication through a rigorous pipeline of multiple stages to accomplish the best quality for model training, including language identification, URL-based filtering, metric-based cleaning, document refinement, and data deduplication. CulturaX is released in Hugging Face facilitate research and advancements in multilingual LLMs: https://huggingface.co/datasets/uonlp/CulturaX.", }

Extensive training datasets represent one of the important factors for the impressive learning capabilities of large language models (LLMs). However, these training datasets for current LLMs, especially the recent state-of-the-art models, are often not fully disclosed. Creating training data for high-performing LLMs involves extensive cleaning and deduplication to ensure the necessary level of quality. The lack of transparency for training data has thus hampered research on attributing and addressing hallucination and bias issues in LLMs, hindering replication efforts and further advancements in the community. These challenges become even more pronounced in multilingual learning scenarios, where the available multilingual text datasets are often inadequately collected and cleaned. Consequently, there is a lack of open-source and readily usable dataset to effectively train LLMs in multiple languages. To overcome this issue, we present CulturaX, a substantial multilingual dataset with 6.3 trillion tokens in 167 languages, tailored for LLM development. Our dataset undergoes meticulous cleaning and deduplication through a rigorous pipeline of multiple stages to accomplish the best quality for model training, including language identification, URL-based filtering, metric-based cleaning, document refinement, and data deduplication. CulturaX is released in Hugging Face facilitate research and advancements in multilingual LLMs: https://huggingface.co/datasets/uonlp/CulturaX.

CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages

lrec-main.377

Poster

2309.09400

https://huggingface.co/papers/2309.09400

https://aclanthology.org/2024.lrec-main.378.bib

https://aclanthology.org/2024.lrec-main.378/

@inproceedings{cimitan-etal-2024-curation, title = "Curation of Benchmark Templates for Measuring Gender Bias in Named Entity Recognition Models", author = "Cimitan, Ana and Alves Pinto, Ana and Geierhos, Michaela", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.378", pages = "4238--4246", abstract = "Named Entity Recognition (NER) constitutes a popular machine learning technique that empowers several natural language processing applications. As with other machine learning applications, NER models have been shown to be susceptible to gender bias. The latter is often assessed using benchmark datasets, which in turn are curated specifically for a given Natural Language Processing (NLP) task. In this work, we investigate the robustness of benchmark templates to detect gender bias and propose a novel method to improve the curation of such datasets. The method, based on masked token prediction, aims to filter out benchmark templates with a higher probability of detecting gender bias in NER models. We tested the method for English and German, using the corresponding fine-tuned BERT base model (cased) as the NER model. The gender gaps detected with templates classified as appropriate by the method were statistically larger than those detected with inappropriate templates. The results were similar for both languages and support the use of the proposed method in the curation of templates designed to detect gender bias.", }

Named Entity Recognition (NER) constitutes a popular machine learning technique that empowers several natural language processing applications. As with other machine learning applications, NER models have been shown to be susceptible to gender bias. The latter is often assessed using benchmark datasets, which in turn are curated specifically for a given Natural Language Processing (NLP) task. In this work, we investigate the robustness of benchmark templates to detect gender bias and propose a novel method to improve the curation of such datasets. The method, based on masked token prediction, aims to filter out benchmark templates with a higher probability of detecting gender bias in NER models. We tested the method for English and German, using the corresponding fine-tuned BERT base model (cased) as the NER model. The gender gaps detected with templates classified as appropriate by the method were statistically larger than those detected with inappropriate templates. The results were similar for both languages and support the use of the proposed method in the curation of templates designed to detect gender bias.

Curation of Benchmark Templates for Measuring Gender Bias in Named Entity Recognition Models

lrec-main.378

Poster

https://aclanthology.org/2024.lrec-main.379.bib

https://aclanthology.org/2024.lrec-main.379/

@inproceedings{kranzlein-etal-2024-curiam, title = "{C}u{RIAM}: Corpus Re Interpretation and Metalanguage in {U}.{S}. {S}upreme {C}ourt Opinions", author = "Kranzlein, Michael and Schneider, Nathan and Tobia, Kevin", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.379", pages = "4247--4258", abstract = "Most judicial decisions involve the interpretation of legal texts. As such, judicial opinions use language as the medium to comment on or draw attention to other language (for example, through definitions and hypotheticals about the meaning of a term from a statute). Language used this way is called metalanguage. Focusing on the U.S. Supreme Court, we view metalanguage as reflective of justices{'} interpretive processes, bearing on current debates and theories about textualism in law and political science. As a step towards large-scale metalinguistic analysis with NLP, we identify 9 categories prominent in metalinguistic discussions, including key terms, definitions, and different kinds of sources. We annotate these concepts in a corpus of U.S. Supreme Court opinions. Our analysis of the corpus reveals high interannotator agreement, frequent use of quotes and sources, and several notable frequency differences between majority, concurring, and dissenting opinions. We observe fewer instances than expected of several legal interpretive categories. We discuss some of the challenges in developing the annotation schema and applying it and provide recommendations for how this corpus can be used for broader analyses.", }

Most judicial decisions involve the interpretation of legal texts. As such, judicial opinions use language as the medium to comment on or draw attention to other language (for example, through definitions and hypotheticals about the meaning of a term from a statute). Language used this way is called metalanguage. Focusing on the U.S. Supreme Court, we view metalanguage as reflective of justices{'} interpretive processes, bearing on current debates and theories about textualism in law and political science. As a step towards large-scale metalinguistic analysis with NLP, we identify 9 categories prominent in metalinguistic discussions, including key terms, definitions, and different kinds of sources. We annotate these concepts in a corpus of U.S. Supreme Court opinions. Our analysis of the corpus reveals high interannotator agreement, frequent use of quotes and sources, and several notable frequency differences between majority, concurring, and dissenting opinions. We observe fewer instances than expected of several legal interpretive categories. We discuss some of the challenges in developing the annotation schema and applying it and provide recommendations for how this corpus can be used for broader analyses.

CuRIAM: Corpus Re Interpretation and Metalanguage in U.S. Supreme Court Opinions

lrec-main.379

Poster

2305.14719

https://aclanthology.org/2024.lrec-main.380.bib

https://aclanthology.org/2024.lrec-main.380/

@inproceedings{nguyen-etal-2024-curriculum, title = "Curriculum Learning Meets Directed Acyclic Graph for Multimodal Emotion Recognition", author = "Nguyen, Cam-Van Thi and Nguyen, Cao-Bach and Le, Duc-Trong and Ha, Quang-Thuy", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.380", pages = "4259--4265", abstract = "Emotion recognition in conversation (ERC) is a crucial task in natural language processing and affective computing. This paper proposes MultiDAG+CL, a novel approach for Multimodal Emotion Recognition in Conversation (ERC) that employs Directed Acyclic Graph (DAG) to integrate textual, acoustic, and visual features within a unified framework. The model is enhanced by Curriculum Learning (CL) to address challenges related to emotional shifts and data imbalance. Curriculum learning facilitates the learning process by gradually presenting training samples in a meaningful order, thereby improving the model{'}s performance in handling emotional variations and data imbalance. Experimental results on the IEMOCAP and MELD datasets demonstrate that the MultiDAG+CL models outperform baseline models. We release the code for and experiments: \url{https://github.com/vanntc711/MultiDAG-CL}.", }

Emotion recognition in conversation (ERC) is a crucial task in natural language processing and affective computing. This paper proposes MultiDAG+CL, a novel approach for Multimodal Emotion Recognition in Conversation (ERC) that employs Directed Acyclic Graph (DAG) to integrate textual, acoustic, and visual features within a unified framework. The model is enhanced by Curriculum Learning (CL) to address challenges related to emotional shifts and data imbalance. Curriculum learning facilitates the learning process by gradually presenting training samples in a meaningful order, thereby improving the model{'}s performance in handling emotional variations and data imbalance. Experimental results on the IEMOCAP and MELD datasets demonstrate that the MultiDAG+CL models outperform baseline models. We release the code for and experiments: \url{https://github.com/vanntc711/MultiDAG-CL}.

Curriculum Learning Meets Directed Acyclic Graph for Multimodal Emotion Recognition

lrec-main.380

Poster

2402.17269

https://aclanthology.org/2024.lrec-main.381.bib

https://aclanthology.org/2024.lrec-main.381/

@inproceedings{t-y-s-s-etal-2024-cusines, title = "{C}u{SIN}e{S}: Curriculum-driven Structure Induced Negative Sampling for Statutory Article Retrieval", author = "T.y.s.s., Santosh and Kaiser, Kristina and Grabmair, Matthias", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.381", pages = "4266--4272", abstract = "In this paper, we introduce CuSINeS, a negative sampling approach to enhance the performance of Statutory Article Retrieval (SAR). CuSINeS offers three key contributions. Firstly, it employs a curriculum-based negative sampling strategy guiding the model to focus on easier negatives initially and progressively tackle more difficult ones. Secondly, it leverages the hierarchical and sequential information derived from the structural organization of statutes to evaluate the difficulty of samples. Lastly, it introduces a dynamic semantic difficulty assessment using the being-trained model itself, surpassing conventional static methods like BM25, adapting the negatives to the model{'}s evolving competence. Experimental results on a real-world expert-annotated SAR dataset validate the effectiveness of CuSINeS across four different baselines, demonstrating its versatility.", }

In this paper, we introduce CuSINeS, a negative sampling approach to enhance the performance of Statutory Article Retrieval (SAR). CuSINeS offers three key contributions. Firstly, it employs a curriculum-based negative sampling strategy guiding the model to focus on easier negatives initially and progressively tackle more difficult ones. Secondly, it leverages the hierarchical and sequential information derived from the structural organization of statutes to evaluate the difficulty of samples. Lastly, it introduces a dynamic semantic difficulty assessment using the being-trained model itself, surpassing conventional static methods like BM25, adapting the negatives to the model{'}s evolving competence. Experimental results on a real-world expert-annotated SAR dataset validate the effectiveness of CuSINeS across four different baselines, demonstrating its versatility.

CuSINeS: Curriculum-driven Structure Induced Negative Sampling for Statutory Article Retrieval

lrec-main.381

Poster

2404.00590

https://aclanthology.org/2024.lrec-main.382.bib

https://aclanthology.org/2024.lrec-main.382/

@inproceedings{fang-etal-2024-cwtm, title = "{CWTM}: Leveraging Contextualized Word Embeddings from {BERT} for Neural Topic Modeling", author = "Fang, Zheng and He, Yulan and Procter, Rob", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.382", pages = "4273--4286", abstract = "Most existing topic models rely on bag-of-words (BOW) representation, which limits their ability to capture word order information and leads to challenges with out-of-vocabulary (OOV) words in new documents. Contextualized word embeddings, however, show superiority in word sense disambiguation and effectively address the OOV issue. In this work, we introduce a novel neural topic model called the Contextlized Word Topic Model (CWTM), which integrates contextualized word embeddings from BERT. The model is capable of learning the topic vector of a document without BOW information. In addition, it can also derive the topic vectors for individual words within a document based on their contextualized word embeddings. Experiments across various datasets show that CWTM generates more coherent and meaningful topics compared to existing topic models, while also accommodating unseen words in newly encountered documents.", }

Most existing topic models rely on bag-of-words (BOW) representation, which limits their ability to capture word order information and leads to challenges with out-of-vocabulary (OOV) words in new documents. Contextualized word embeddings, however, show superiority in word sense disambiguation and effectively address the OOV issue. In this work, we introduce a novel neural topic model called the Contextlized Word Topic Model (CWTM), which integrates contextualized word embeddings from BERT. The model is capable of learning the topic vector of a document without BOW information. In addition, it can also derive the topic vectors for individual words within a document based on their contextualized word embeddings. Experiments across various datasets show that CWTM generates more coherent and meaningful topics compared to existing topic models, while also accommodating unseen words in newly encountered documents.

CWTM: Leveraging Contextualized Word Embeddings from BERT for Neural Topic Modeling

lrec-main.382

Poster

2305.09329

https://aclanthology.org/2024.lrec-main.383.bib

https://aclanthology.org/2024.lrec-main.383/

@inproceedings{ollagnier-2024-cyberagressionado, title = "{C}yber{A}gression{A}do-v2: Leveraging Pragmatic-Level Information to Decipher Online Hate in {F}rench Multiparty Chats", author = "Ollagnier, Anais", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.383", pages = "4287--4298", abstract = "As a part of the release of the \textit{CyberAgressionAdo-V2} dataset, this paper introduces a new tagset that includes tags marking pragmatic-level information occurring in cyberbullying situations. The previous version of this dataset, \textit{CyberAgressionAdo-V1}, consists of aggressive multiparty chats in French annotated using a hierarchical tagset developed to describe bullying narrative events including the participant roles, the presence of hate speech, the type of verbal abuse, among others. In contrast, \textit{CyberAgressionAdo-V2} uses a multi-label, fine-grained tagset marking the discursive role of exchanged messages as well as the context in which they occur {---} for instance, attack (ATK), defend (DFN), counterspeech (CNS), abet/instigate (AIN), gaslight (GSL), etc. This paper provides a comprehensive overview of the annotation tagset and presents statistical insights derived from its application. Additionally, we address the challenges encountered when annotating pragmatic-level information in this context, conducting a thorough analysis of annotator disagreements. The resulting dataset comprises 19 conversations that have been manually annotated and is now available to facilitate further research in the field.", }

As a part of the release of the \textit{CyberAgressionAdo-V2} dataset, this paper introduces a new tagset that includes tags marking pragmatic-level information occurring in cyberbullying situations. The previous version of this dataset, \textit{CyberAgressionAdo-V1}, consists of aggressive multiparty chats in French annotated using a hierarchical tagset developed to describe bullying narrative events including the participant roles, the presence of hate speech, the type of verbal abuse, among others. In contrast, \textit{CyberAgressionAdo-V2} uses a multi-label, fine-grained tagset marking the discursive role of exchanged messages as well as the context in which they occur {---} for instance, attack (ATK), defend (DFN), counterspeech (CNS), abet/instigate (AIN), gaslight (GSL), etc. This paper provides a comprehensive overview of the annotation tagset and presents statistical insights derived from its application. Additionally, we address the challenges encountered when annotating pragmatic-level information in this context, conducting a thorough analysis of annotator disagreements. The resulting dataset comprises 19 conversations that have been manually annotated and is now available to facilitate further research in the field.

CyberAgressionAdo-v2: Leveraging Pragmatic-Level Information to Decipher Online Hate in French Multiparty Chats

lrec-main.383

Poster

https://aclanthology.org/2024.lrec-main.384.bib

https://aclanthology.org/2024.lrec-main.384/

@inproceedings{smid-etal-2024-czech, title = "{C}zech Dataset for Complex Aspect-Based Sentiment Analysis Tasks", author = "{\v{S}}m{\'\i}d, Jakub and P{\v{r}}ib{\'a}{\v{n}}, Pavel and Prazak, Ondrej and Kral, Pavel", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.384", pages = "4299--4310", abstract = "In this paper, we introduce a novel Czech dataset for aspect-based sentiment analysis (ABSA), which consists of 3.1K manually annotated reviews from the restaurant domain. The dataset is built upon the older Czech dataset, which contained only separate labels for the basic ABSA tasks such as aspect term extraction or aspect polarity detection. Unlike its predecessor, our new dataset is specifically designed to allow its usage for more complex tasks, e.g. target-aspect-category detection. These advanced tasks require a unified annotation format, seamlessly linking sentiment elements (labels) together. Our dataset follows the format of the well-known SemEval-2016 datasets. This design choice allows effortless application and evaluation in cross-lingual scenarios, ultimately fostering cross-language comparisons with equivalent counterpart datasets in other languages. The annotation process engaged two trained annotators, yielding an impressive inter-annotator agreement rate of approximately 90{\%}. Additionally, we provide 24M reviews without annotations suitable for unsupervised learning. We present robust monolingual baseline results achieved with various Transformer-based models and insightful error analysis to supplement our contributions. Our code and dataset are freely available for non-commercial research purposes.", }

In this paper, we introduce a novel Czech dataset for aspect-based sentiment analysis (ABSA), which consists of 3.1K manually annotated reviews from the restaurant domain. The dataset is built upon the older Czech dataset, which contained only separate labels for the basic ABSA tasks such as aspect term extraction or aspect polarity detection. Unlike its predecessor, our new dataset is specifically designed to allow its usage for more complex tasks, e.g. target-aspect-category detection. These advanced tasks require a unified annotation format, seamlessly linking sentiment elements (labels) together. Our dataset follows the format of the well-known SemEval-2016 datasets. This design choice allows effortless application and evaluation in cross-lingual scenarios, ultimately fostering cross-language comparisons with equivalent counterpart datasets in other languages. The annotation process engaged two trained annotators, yielding an impressive inter-annotator agreement rate of approximately 90{\%}. Additionally, we provide 24M reviews without annotations suitable for unsupervised learning. We present robust monolingual baseline results achieved with various Transformer-based models and insightful error analysis to supplement our contributions. Our code and dataset are freely available for non-commercial research purposes.

Czech Dataset for Complex Aspect-Based Sentiment Analysis Tasks

lrec-main.384

Poster

https://aclanthology.org/2024.lrec-main.385.bib

https://aclanthology.org/2024.lrec-main.385/

@inproceedings{chaudhury-etal-2024-dacl, title = "{DACL}: Disfluency Augmented Curriculum Learning for Fluent Text Generation", author = "Chaudhury, Rohan and Teleki, Maria and Dong, Xiangjue and Caverlee, James", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.385", pages = "4311--4321", abstract = "Voice-driven software systems are in abundance. However, language models that power these systems are traditionally trained on fluent, written text corpora. Hence there can be a misalignment between the inherent disfluency of transcribed spoken content and the fluency of the written training data. Furthermore, gold-standard disfluency annotations of various complexities for incremental training can be expensive to collect. So, we propose in this paper a Disfluency Augmented Curriculum Learning (DACL) approach to tackle the complex structure of disfluent sentences and generate fluent texts from them, by using Curriculum Learning (CL) coupled with our synthetically augmented disfluent texts of various levels. DACL harnesses the tiered structure of our generated synthetic disfluent data using CL, by training the model on basic samples (i.e. more fluent) first before training it on more complex samples (i.e. more disfluent). In contrast to the random data exposure paradigm, DACL focuses on a simple-to-complex learning process. We comprehensively evaluate DACL on Switchboard Penn Treebank-3 and compare it to the state-of-the-art disfluency removal models. Our model surpasses existing techniques in word-based precision (by up to 1{\%}) and has shown favorable recall and F1 scores.", }

Voice-driven software systems are in abundance. However, language models that power these systems are traditionally trained on fluent, written text corpora. Hence there can be a misalignment between the inherent disfluency of transcribed spoken content and the fluency of the written training data. Furthermore, gold-standard disfluency annotations of various complexities for incremental training can be expensive to collect. So, we propose in this paper a Disfluency Augmented Curriculum Learning (DACL) approach to tackle the complex structure of disfluent sentences and generate fluent texts from them, by using Curriculum Learning (CL) coupled with our synthetically augmented disfluent texts of various levels. DACL harnesses the tiered structure of our generated synthetic disfluent data using CL, by training the model on basic samples (i.e. more fluent) first before training it on more complex samples (i.e. more disfluent). In contrast to the random data exposure paradigm, DACL focuses on a simple-to-complex learning process. We comprehensively evaluate DACL on Switchboard Penn Treebank-3 and compare it to the state-of-the-art disfluency removal models. Our model surpasses existing techniques in word-based precision (by up to 1{\%}) and has shown favorable recall and F1 scores.

DACL: Disfluency Augmented Curriculum Learning for Fluent Text Generation

lrec-main.385

Poster

https://aclanthology.org/2024.lrec-main.386.bib

https://aclanthology.org/2024.lrec-main.386/

@inproceedings{lupo-etal-2024-dadit, title = "{DADIT}: A Dataset for Demographic Classification of {I}talian {T}witter Users and a Comparison of Prediction Methods", author = "Lupo, Lorenzo and Bose, Paul and Habibi, Mahyar and Hovy, Dirk and Schwarz, Carlo", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.386", pages = "4322--4332", abstract = "Social scientists increasingly use demographically stratified social media data to study the attitudes, beliefs, and behavior of the general public. To facilitate such analyses, we construct, validate, and release publicly the representative DADIT dataset of 30M tweets of 20k Italian Twitter users, along with their bios and profile pictures. We enrich the user data with high-quality labels for gender, age, and location. DADIT enables us to train and compare the performance of various state-of-the-art models for the prediction of the gender and age of social media users. In particular, we investigate if tweets contain valuable information for the task, since popular classifiers like M3 don{'}t leverage them. Our best XLM-based classifier improves upon the commonly used competitor M3 by up to 53{\%} F1. Especially for age prediction, classifiers profit from including tweets as features. We also confirm these findings on a German test set.", }

Social scientists increasingly use demographically stratified social media data to study the attitudes, beliefs, and behavior of the general public. To facilitate such analyses, we construct, validate, and release publicly the representative DADIT dataset of 30M tweets of 20k Italian Twitter users, along with their bios and profile pictures. We enrich the user data with high-quality labels for gender, age, and location. DADIT enables us to train and compare the performance of various state-of-the-art models for the prediction of the gender and age of social media users. In particular, we investigate if tweets contain valuable information for the task, since popular classifiers like M3 don{'}t leverage them. Our best XLM-based classifier improves upon the commonly used competitor M3 by up to 53{\%} F1. Especially for age prediction, classifiers profit from including tweets as features. We also confirm these findings on a German test set.

DADIT: A Dataset for Demographic Classification of Italian Twitter Users and a Comparison of Prediction Methods

lrec-main.386

Poster

2403.05700

https://huggingface.co/papers/2403.05700

https://aclanthology.org/2024.lrec-main.387.bib

https://aclanthology.org/2024.lrec-main.387/

@inproceedings{wang-etal-2024-dancer, title = "{DANCER}: Entity Description Augmented Named Entity Corrector for Automatic Speech Recognition", author = "Wang, Yi-Cheng and Wang, Hsin-Wei and Yan, Bi-Cheng and Lin, Chi-Han and Chen, Berlin", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.387", pages = "4333--4342", abstract = "End-to-end automatic speech recognition (E2E ASR) systems often suffer from mistranscription of domain-specific phrases, such as named entities, sometimes leading to catastrophic failures in downstream tasks. A family of fast and lightweight named entity correction (NEC) models for ASR have recently been proposed, which normally build on pho-netic-level edit distance algorithms and have shown impressive NEC performance. However, as the named entity (NE) list grows, the problems of phonetic confusion in the NE list are exacerbated; for example, homophone ambiguities increase substantially. In view of this, we proposed a novel Description Augmented Named entity CorrEctoR (dubbed DANCER), which leverages entity descriptions to provide additional information to facilitate mitigation of phonetic con-fusion for NEC on ASR transcription. To this end, an efficient entity description augmented masked language model (EDA-MLM) comprised of a dense retrieval model is introduced, enabling MLM to adapt swiftly to domain-specific entities for the NEC task. A series of experiments conducted on the AISHELL-1 and Homophone datasets confirm the effectiveness of our modeling approach. DANCER outperforms a strong baseline, the phonetic edit-distance-based NEC model (PED-NEC), by a character error rate (CER) reduction of about 7{\%} relatively on AISHELL-1 for named entities. More notably, when tested on Homophone that contain named entities of high phonetic confusion, DANCER offers a more pronounced CER reduction of 46{\%} relatively over PED-NEC for named entities. The code is available at https://github.com/Amiannn/Dancer.", }

End-to-end automatic speech recognition (E2E ASR) systems often suffer from mistranscription of domain-specific phrases, such as named entities, sometimes leading to catastrophic failures in downstream tasks. A family of fast and lightweight named entity correction (NEC) models for ASR have recently been proposed, which normally build on pho-netic-level edit distance algorithms and have shown impressive NEC performance. However, as the named entity (NE) list grows, the problems of phonetic confusion in the NE list are exacerbated; for example, homophone ambiguities increase substantially. In view of this, we proposed a novel Description Augmented Named entity CorrEctoR (dubbed DANCER), which leverages entity descriptions to provide additional information to facilitate mitigation of phonetic con-fusion for NEC on ASR transcription. To this end, an efficient entity description augmented masked language model (EDA-MLM) comprised of a dense retrieval model is introduced, enabling MLM to adapt swiftly to domain-specific entities for the NEC task. A series of experiments conducted on the AISHELL-1 and Homophone datasets confirm the effectiveness of our modeling approach. DANCER outperforms a strong baseline, the phonetic edit-distance-based NEC model (PED-NEC), by a character error rate (CER) reduction of about 7{\%} relatively on AISHELL-1 for named entities. More notably, when tested on Homophone that contain named entities of high phonetic confusion, DANCER offers a more pronounced CER reduction of 46{\%} relatively over PED-NEC for named entities. The code is available at https://github.com/Amiannn/Dancer.

DANCER: Entity Description Augmented Named Entity Corrector for Automatic Speech Recognition

lrec-main.387

Poster

2403.17645

https://aclanthology.org/2024.lrec-main.388.bib

https://aclanthology.org/2024.lrec-main.388/

@inproceedings{bacciu-etal-2024-dantellm, title = "{D}ante{LLM}: Let{'}s Push {I}talian {LLM} Research Forward!", author = "Bacciu, Andrea and Campagnano, Cesare and Trappolini, Giovanni and Silvestri, Fabrizio", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.388", pages = "4343--4355", abstract = "In recent years, the dominance of Large Language Models (LLMs) in the English language has become evident. However, there remains a pronounced gap in resources and evaluation tools tailored for non-English languages, underscoring a significant disparity in the global AI landscape. This paper seeks to bridge this gap, specifically focusing on the Italian linguistic context. We introduce a novel benchmark, and an open LLM Leaderboard, designed to evaluate LLMs{'} performance in Italian, providing a rigorous framework for comparative analysis. In our assessment of currently available models, we highlight their respective strengths and limitations against this standard. Crucially, we propose {``}DanteLLM{''}, a state-of-the-art LLM dedicated to Italian. Our empirical evaluations underscore Dante{'}s superiority, as it emerges as the most performant model on our benchmark, with improvements by up to 6 points. This research not only marks a significant stride in Italian-centric natural language processing but also offers a blueprint for the development and evaluation of LLMs in other languages, championing a more inclusive AI paradigm. Our code at: https://github.com/RSTLess-research/DanteLLM", }

In recent years, the dominance of Large Language Models (LLMs) in the English language has become evident. However, there remains a pronounced gap in resources and evaluation tools tailored for non-English languages, underscoring a significant disparity in the global AI landscape. This paper seeks to bridge this gap, specifically focusing on the Italian linguistic context. We introduce a novel benchmark, and an open LLM Leaderboard, designed to evaluate LLMs{'} performance in Italian, providing a rigorous framework for comparative analysis. In our assessment of currently available models, we highlight their respective strengths and limitations against this standard. Crucially, we propose {``}DanteLLM{''}, a state-of-the-art LLM dedicated to Italian. Our empirical evaluations underscore Dante{'}s superiority, as it emerges as the most performant model on our benchmark, with improvements by up to 6 points. This research not only marks a significant stride in Italian-centric natural language processing but also offers a blueprint for the development and evaluation of LLMs in other languages, championing a more inclusive AI paradigm. Our code at: https://github.com/RSTLess-research/DanteLLM

DanteLLM: Let's Push Italian LLM Research Forward!

lrec-main.388

Poster

https://aclanthology.org/2024.lrec-main.389.bib

https://aclanthology.org/2024.lrec-main.389/

@inproceedings{schaller-etal-2024-darius, title = "{DARIUS}: A Comprehensive Learner Corpus for Argument Mining in {G}erman-Language Essays", author = {Schaller, Nils-Jonathan and Horbach, Andrea and H{\"o}ft, Lars Ingver and Ding, Yuning and Bahr, Jan Luca and Meyer, Jennifer and Jansen, Thorben}, editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.389", pages = "4356--4367", abstract = "In this paper, we present the DARIUS (Digital Argumentation Instruction for Science) corpus for argumentation quality on 4589 essays written by 1839 German secondary school students. The corpus is annotated according to a fine-grained annotation scheme, ranging from a broader perspective like content zones, to more granular features like argumentation coverage/reach and argumentative discourse units like claims and warrants. The features have inter-annotator agreements up to 0.83 Krippendorff{'}s α. The corpus and dataset are publicly available for further research in argument mining.", }

In this paper, we present the DARIUS (Digital Argumentation Instruction for Science) corpus for argumentation quality on 4589 essays written by 1839 German secondary school students. The corpus is annotated according to a fine-grained annotation scheme, ranging from a broader perspective like content zones, to more granular features like argumentation coverage/reach and argumentative discourse units like claims and warrants. The features have inter-annotator agreements up to 0.83 Krippendorff{'}s α. The corpus and dataset are publicly available for further research in argument mining.

DARIUS: A Comprehensive Learner Corpus for Argument Mining in German-Language Essays

lrec-main.389

Poster

https://aclanthology.org/2024.lrec-main.390.bib

https://aclanthology.org/2024.lrec-main.390/

@inproceedings{de-jesus-nunes-2024-data, title = "Data Collection Pipeline for Low-Resource Languages: A Case Study on Constructing a Tetun Text Corpus", author = "de Jesus, Gabriel and Nunes, S{\'e}rgio Sobral", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.390", pages = "4368--4380", abstract = "This paper proposes Labadain Crawler, a data collection pipeline tailored to automate and optimize the process of constructing textual corpora from the web, with a specific target to low-resource languages. The system is built on top of Nutch, an open-source web crawler and data extraction framework, and incorporates language processing components such as a tokenizer and a language identification model. The pipeline efficacy is demonstrated through successful testing with Tetun, one of Timor-Leste{'}s official languages, resulting in the construction of a high-quality Tetun text corpus comprising 321.7k sentences extracted from over 22k web pages. The contributions of this paper include the development of a Tetun tokenizer, a Tetun language identification model, and a Tetun text corpus, marking an important milestone in Tetun text information retrieval.", }

This paper proposes Labadain Crawler, a data collection pipeline tailored to automate and optimize the process of constructing textual corpora from the web, with a specific target to low-resource languages. The system is built on top of Nutch, an open-source web crawler and data extraction framework, and incorporates language processing components such as a tokenizer and a language identification model. The pipeline efficacy is demonstrated through successful testing with Tetun, one of Timor-Leste{'}s official languages, resulting in the construction of a high-quality Tetun text corpus comprising 321.7k sentences extracted from over 22k web pages. The contributions of this paper include the development of a Tetun tokenizer, a Tetun language identification model, and a Tetun text corpus, marking an important milestone in Tetun text information retrieval.

Data Collection Pipeline for Low-Resource Languages: A Case Study on Constructing a Tetun Text Corpus

lrec-main.390

Poster

https://aclanthology.org/2024.lrec-main.391.bib

https://aclanthology.org/2024.lrec-main.391/

@inproceedings{grundmann-etal-2024-data, title = "Data Drift in Clinical Outcome Prediction from Admission Notes", author = "Grundmann, Paul and Papaioannou, Jens-Michalis and Oberhauser, Tom and Steffek, Thomas and Siu, Amy and Nejdl, Wolfgang and Loeser, Alexander", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.391", pages = "4381--4391", abstract = "Clinical NLP research faces a scarcity of publicly available datasets due to privacy concerns. MIMIC-III marked a significant milestone, enabling substantial progress, and now, with MIMIC-IV, the dataset has expanded significantly, offering a broader scope. In this paper, we focus on the task of predicting clinical outcomes from clinical text. This is crucial in modern healthcare, aiding in preventive care, differential diagnosis, and capacity planning. We introduce a novel clinical outcome prediction dataset derived from MIMIC-IV. Furthermore, we provide initial insights into the performance of models trained on MIMIC-III when applied to our new dataset, with specific attention to potential data drift. We investigate challenges tied to evolving documentation standards and changing codes in the International Classification of Diseases (ICD) taxonomy, such as the transition from ICD-9 to ICD-10. We also explore variations in clinical text across different hospital wards. Our study aims to probe the robustness and generalization of clinical outcome prediction models, contributing to the ongoing advancement of clinical NLP in healthcare.", }

Clinical NLP research faces a scarcity of publicly available datasets due to privacy concerns. MIMIC-III marked a significant milestone, enabling substantial progress, and now, with MIMIC-IV, the dataset has expanded significantly, offering a broader scope. In this paper, we focus on the task of predicting clinical outcomes from clinical text. This is crucial in modern healthcare, aiding in preventive care, differential diagnosis, and capacity planning. We introduce a novel clinical outcome prediction dataset derived from MIMIC-IV. Furthermore, we provide initial insights into the performance of models trained on MIMIC-III when applied to our new dataset, with specific attention to potential data drift. We investigate challenges tied to evolving documentation standards and changing codes in the International Classification of Diseases (ICD) taxonomy, such as the transition from ICD-9 to ICD-10. We also explore variations in clinical text across different hospital wards. Our study aims to probe the robustness and generalization of clinical outcome prediction models, contributing to the ongoing advancement of clinical NLP in healthcare.

Data Drift in Clinical Outcome Prediction from Admission Notes

lrec-main.391

Poster

https://aclanthology.org/2024.lrec-main.392.bib

https://aclanthology.org/2024.lrec-main.392/

@inproceedings{rugina-etal-2024-data, title = "Data-Informed Global Sparseness in Attention Mechanisms for Deep Neural Networks", author = "Rugina, Ileana and Dangovski, Rumen and Jing, Li and Nakov, Preslav and Soljacic, Marin", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.392", pages = "4392--4403", abstract = "Attention mechanisms play a crucial role in the neural revolution of Natural Language Processing (NLP). With the growth of attention-based models, several pruning techniques have been developed to identify and exploit sparseness, making these models more efficient. Most efforts focus on hard-coding attention patterns or pruning attention weights based on training data. We propose Attention Pruning (AP), a framework that observes attention patterns in a fixed dataset and generates a global sparseness mask. AP saves 90{\%} of attention computation for language modeling and about 50{\%} for machine translation and GLUE tasks, maintaining result quality. Our method reveals important distinctions between self- and cross-attention patterns, guiding future NLP research. Our framework can reduce both latency and memory requirements for any attention-based model, aiding in the development of improved models for existing or new NLP applications. We have demonstrated this with encoder and autoregressive transformer models using Triton GPU kernels and make our code publicly available at https://github.com/irugina/AP", }

Attention mechanisms play a crucial role in the neural revolution of Natural Language Processing (NLP). With the growth of attention-based models, several pruning techniques have been developed to identify and exploit sparseness, making these models more efficient. Most efforts focus on hard-coding attention patterns or pruning attention weights based on training data. We propose Attention Pruning (AP), a framework that observes attention patterns in a fixed dataset and generates a global sparseness mask. AP saves 90{\%} of attention computation for language modeling and about 50{\%} for machine translation and GLUE tasks, maintaining result quality. Our method reveals important distinctions between self- and cross-attention patterns, guiding future NLP research. Our framework can reduce both latency and memory requirements for any attention-based model, aiding in the development of improved models for existing or new NLP applications. We have demonstrated this with encoder and autoregressive transformer models using Triton GPU kernels and make our code publicly available at https://github.com/irugina/AP

Data-Informed Global Sparseness in Attention Mechanisms for Deep Neural Networks

lrec-main.392

Poster

2012.02030

https://aclanthology.org/2024.lrec-main.393.bib

https://aclanthology.org/2024.lrec-main.393/

@inproceedings{kumaresan-etal-2024-dataset, title = "Dataset for Identification of Homophobia and Transphobia for {T}elugu, {K}annada, and {G}ujarati", author = "Kumaresan, Prasanna Kumar and Ponnusamy, Rahul and Sharma, Dhruv and Buitelaar, Paul and Chakravarthi, Bharathi Raja", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.393", pages = "4404--4411", abstract = "Users of social media platforms are negatively affected by the proliferation of hate or abusive content. There has been a rise in homophobic and transphobic content in recent years targeting LGBT+ individuals. The increasing levels of homophobia and transphobia online can make online platforms harmful and threatening for LGBT+ persons, potentially inhibiting equality, diversity, and inclusion. We are introducing a new dataset for three languages, namely Telugu, Kannada, and Gujarati. Additionally, we have created an expert-labeled dataset to automatically identify homophobic and transphobic content within comments collected from YouTube. We provided comprehensive annotation rules to educate annotators in this process. We collected approximately 10,000 comments from YouTube for all three languages. Marking the first dataset of these languages for this task, we also developed a baseline model with pre-trained transformers.", }

Users of social media platforms are negatively affected by the proliferation of hate or abusive content. There has been a rise in homophobic and transphobic content in recent years targeting LGBT+ individuals. The increasing levels of homophobia and transphobia online can make online platforms harmful and threatening for LGBT+ persons, potentially inhibiting equality, diversity, and inclusion. We are introducing a new dataset for three languages, namely Telugu, Kannada, and Gujarati. Additionally, we have created an expert-labeled dataset to automatically identify homophobic and transphobic content within comments collected from YouTube. We provided comprehensive annotation rules to educate annotators in this process. We collected approximately 10,000 comments from YouTube for all three languages. Marking the first dataset of these languages for this task, we also developed a baseline model with pre-trained transformers.

Dataset for Identification of Homophobia and Transphobia for Telugu, Kannada, and Gujarati

lrec-main.393

Poster

https://aclanthology.org/2024.lrec-main.394.bib

https://aclanthology.org/2024.lrec-main.394/

@inproceedings{petersen-frey-biemann-2024-dataset, title = "Dataset of Quotation Attribution in {G}erman News Articles", author = "Petersen-Frey, Fynn and Biemann, Chris", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.394", pages = "4412--4422", abstract = "Extracting who says what to whom is a crucial part in analyzing human communication in today{'}s abundance of data such as online news articles. Yet, the lack of annotated data for this task in German news articles severely limits the quality and usability of possible systems. To remedy this, we present a new, freely available, creative-commons-licensed dataset for quotation attribution in German news articles based on WIKINEWS. The dataset provides curated, high-quality annotations across 1000 documents (250,000 tokens) in a fine-grained annotation schema enabling various downstream uses for the dataset. The annotations not only specify who said what but also how, in which context, to whom and define the type of quotation. We specify our annotation schema, describe the creation of the dataset and provide a quantitative analysis. Further, we describe suitable evaluation metrics, apply two existing systems for quotation attribution, discuss their results to evaluate the utility of our dataset and outline use cases of our dataset in downstream tasks.", }

Extracting who says what to whom is a crucial part in analyzing human communication in today{'}s abundance of data such as online news articles. Yet, the lack of annotated data for this task in German news articles severely limits the quality and usability of possible systems. To remedy this, we present a new, freely available, creative-commons-licensed dataset for quotation attribution in German news articles based on WIKINEWS. The dataset provides curated, high-quality annotations across 1000 documents (250,000 tokens) in a fine-grained annotation schema enabling various downstream uses for the dataset. The annotations not only specify who said what but also how, in which context, to whom and define the type of quotation. We specify our annotation schema, describe the creation of the dataset and provide a quantitative analysis. Further, we describe suitable evaluation metrics, apply two existing systems for quotation attribution, discuss their results to evaluate the utility of our dataset and outline use cases of our dataset in downstream tasks.

Dataset of Quotation Attribution in German News Articles

lrec-main.394

Poster

2404.16764

https://huggingface.co/papers/2404.16764

https://aclanthology.org/2024.lrec-main.395.bib

https://aclanthology.org/2024.lrec-main.395/

@inproceedings{yan-etal-2024-dc, title = "{DC}-{MBR}: Distributional Cooling for Minimum {B}ayesian Risk Decoding", author = "Yan, Jianhao and Xu, Jin and Meng, Fandong and Zhou, Jie and Zhang, Yue", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.395", pages = "4423--4437", abstract = "Minimum Bayesian Risk Decoding (MBR) emerges as a promising decoding algorithm in Neural Machine Translation. However, MBR performs poorly with label smoothing, which is surprising as label smoothing provides decent improvement with beam search and improves generality in various tasks. In this work, we show that the issue arises from the inconsistency of label smoothing on the token-level and sequence-level distributions. We demonstrate that even though label smoothing only causes a slight change in the token level, the sequence-level distribution is highly skewed. We coin the issue \textit{autoregressive over-smoothness}. To address this issue, we propose a simple and effective method, Distributional Cooling MBR (DC-MBR), which manipulates the entropy of output distributions by tuning down the Softmax temperature. We theoretically prove the equivalence between the pre-tuning label smoothing factor and distributional cooling. Extensive experiments on NMT benchmarks validate that distributional cooling improves MBR in various settings.", }

Minimum Bayesian Risk Decoding (MBR) emerges as a promising decoding algorithm in Neural Machine Translation. However, MBR performs poorly with label smoothing, which is surprising as label smoothing provides decent improvement with beam search and improves generality in various tasks. In this work, we show that the issue arises from the inconsistency of label smoothing on the token-level and sequence-level distributions. We demonstrate that even though label smoothing only causes a slight change in the token level, the sequence-level distribution is highly skewed. We coin the issue \textit{autoregressive over-smoothness}. To address this issue, we propose a simple and effective method, Distributional Cooling MBR (DC-MBR), which manipulates the entropy of output distributions by tuning down the Softmax temperature. We theoretically prove the equivalence between the pre-tuning label smoothing factor and distributional cooling. Extensive experiments on NMT benchmarks validate that distributional cooling improves MBR in various settings.

DC-MBR: Distributional Cooling for Minimum Bayesian Risk Decoding

lrec-main.395

Poster

2212.04205

https://aclanthology.org/2024.lrec-main.396.bib

https://aclanthology.org/2024.lrec-main.396/

@inproceedings{winter-etal-2024-ddxgym, title = "{DD}x{G}ym: Online Transformer Policies in a Knowledge Graph Based Natural Language Environment", author = "Winter, Benjamin and Figueroa Rosero, Alexei Gustavo and Loeser, Alexander and Gers, Felix Alexander and Figueroa Rosero, Nancy Katerina and Krestel, Ralf", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.396", pages = "4438--4448", abstract = "Differential diagnosis (DDx) is vital for physicians and challenging due to the existence of numerous diseases and their complex symptoms. Model training for this task is generally hindered by limited data access due to privacy concerns. To address this, we present DDxGym, a specialized OpenAI Gym environment for clinical differential diagnosis. DDxGym formulates DDx as a natural-language-based reinforcement learning (RL) problem, where agents emulate medical professionals, selecting examinations and treatments for patients with randomly sampled diseases. This RL environment utilizes data labeled from online resources, evaluated by medical professionals for accuracy. Transformers, while effective for encoding text in DDxGym, are unstable in online RL. For that reason we propose a novel training method using an auxiliary masked language modeling objective for policy optimization, resulting in model stabilization and significant performance improvement over strong baselines. Following this approach, our agent effectively navigates large action spaces and identifies universally applicable actions. All data, environment details, and implementation, including experiment reproduction code, are made publicly available.", }

Differential diagnosis (DDx) is vital for physicians and challenging due to the existence of numerous diseases and their complex symptoms. Model training for this task is generally hindered by limited data access due to privacy concerns. To address this, we present DDxGym, a specialized OpenAI Gym environment for clinical differential diagnosis. DDxGym formulates DDx as a natural-language-based reinforcement learning (RL) problem, where agents emulate medical professionals, selecting examinations and treatments for patients with randomly sampled diseases. This RL environment utilizes data labeled from online resources, evaluated by medical professionals for accuracy. Transformers, while effective for encoding text in DDxGym, are unstable in online RL. For that reason we propose a novel training method using an auxiliary masked language modeling objective for policy optimization, resulting in model stabilization and significant performance improvement over strong baselines. Following this approach, our agent effectively navigates large action spaces and identifies universally applicable actions. All data, environment details, and implementation, including experiment reproduction code, are made publicly available.

DDxGym: Online Transformer Policies in a Knowledge Graph Based Natural Language Environment

lrec-main.396

Poster

https://aclanthology.org/2024.lrec-main.397.bib

https://aclanthology.org/2024.lrec-main.397/

@inproceedings{unlu-menevse-etal-2024-dealing, title = "Dealing with Data Scarcity in Spoken Question Answering", author = {{\"U}nl{\"u} Menev{\c{s}}e, Merve and Manav, Yusufcan and Arisoy, Ebru and {\"O}zg{\"u}r, Arzucan}, editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.397", pages = "4449--4455", abstract = "This paper focuses on dealing with data scarcity in spoken question answering (QA) using automatic question-answer generation and a carefully selected fine-tuning strategy that leverages limited annotated data (paragraphs and question-answer pairs). Spoken QA is a challenging task due to using spoken documents, i.e., erroneous automatic speech recognition (ASR) transcriptions, and the scarcity of spoken QA data. We propose a framework for utilizing limited annotated data effectively to improve spoken QA performance. To deal with data scarcity, we train a question-answer generation model with annotated data and then produce large amounts of question-answer pairs from unannotated data (paragraphs). Our experiments demonstrate that incorporating limited annotated data and the automatically generated data through a carefully selected fine-tuning strategy leads to 5.5{\%} relative F1 gain over the model trained only with annotated data. Moreover, the proposed framework is also effective in high ASR errors.", }

This paper focuses on dealing with data scarcity in spoken question answering (QA) using automatic question-answer generation and a carefully selected fine-tuning strategy that leverages limited annotated data (paragraphs and question-answer pairs). Spoken QA is a challenging task due to using spoken documents, i.e., erroneous automatic speech recognition (ASR) transcriptions, and the scarcity of spoken QA data. We propose a framework for utilizing limited annotated data effectively to improve spoken QA performance. To deal with data scarcity, we train a question-answer generation model with annotated data and then produce large amounts of question-answer pairs from unannotated data (paragraphs). Our experiments demonstrate that incorporating limited annotated data and the automatically generated data through a carefully selected fine-tuning strategy leads to 5.5{\%} relative F1 gain over the model trained only with annotated data. Moreover, the proposed framework is also effective in high ASR errors.

Dealing with Data Scarcity in Spoken Question Answering

lrec-main.397

Poster

https://aclanthology.org/2024.lrec-main.398.bib

https://aclanthology.org/2024.lrec-main.398/

@inproceedings{friedman-etal-2024-debiasing, title = "Debiasing Multi-Entity Aspect-Based Sentiment Analysis with Norm-Based Data Augmentation", author = "Friedman, Scott and Zheng, Joan and Steinmetz, Hillel", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.398", pages = "4456--4461", abstract = "Bias in NLP models may arise from using pre-trained transformer models trained on biased corpora, or by training or fine-tuning directly on corpora with systemic biases. Recent research has explored strategies for reduce measurable biases in NLP predictions while maintaining prediction accuracy on held-out test sets, e.g., by modifying word embedding geometry after training, using purpose-built neural modules for training, or automatically augmenting training data with examples designed to reduce bias. This paper focuses on a debiasing strategy for aspect-based sentiment analysis (ABSA) by augmenting the training data using norm-based language templates derived from previous language resources. We show that the baseline model predicts lower sentiment toward some topics and individuals than others and has relatively high prediction bias (measured by standard deviation), even when the context is held constant. Our results show that our norm-based data augmentation reduces topical bias to less than half while maintaining prediction quality (measured by RMSE), by augmenting the training data by only 1.8{\%}.", }

Bias in NLP models may arise from using pre-trained transformer models trained on biased corpora, or by training or fine-tuning directly on corpora with systemic biases. Recent research has explored strategies for reduce measurable biases in NLP predictions while maintaining prediction accuracy on held-out test sets, e.g., by modifying word embedding geometry after training, using purpose-built neural modules for training, or automatically augmenting training data with examples designed to reduce bias. This paper focuses on a debiasing strategy for aspect-based sentiment analysis (ABSA) by augmenting the training data using norm-based language templates derived from previous language resources. We show that the baseline model predicts lower sentiment toward some topics and individuals than others and has relatively high prediction bias (measured by standard deviation), even when the context is held constant. Our results show that our norm-based data augmentation reduces topical bias to less than half while maintaining prediction quality (measured by RMSE), by augmenting the training data by only 1.8{\%}.

Debiasing Multi-Entity Aspect-Based Sentiment Analysis with Norm-Based Data Augmentation

lrec-main.398

Poster

https://aclanthology.org/2024.lrec-main.399.bib

https://aclanthology.org/2024.lrec-main.399/

@inproceedings{picca-pavlopoulos-2024-deciphering, title = "Deciphering Emotional Landscapes in the {I}liad: A Novel {F}rench-Annotated Dataset for Emotion Recognition", author = "Picca, Davide and Pavlopoulos, John", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.399", pages = "4462--4467", abstract = "One of the most significant pieces of ancient Greek literature, the Iliad, is part of humanity{'}s collective cultural heritage. This work aims to provide the scientific community with an emotion-labeled dataset for classical literature and Western mythology in particular. To model the emotions of the poem, we use a multi-variate time series. We also evaluated the dataset by means of two methods. We compare the manual classification against a dictionary-based benchmark as well as employ a state-of-the-art deep learning masked language model that has been tuned using our data. Both evaluations return encouraging results (MSE and MAE Macro Avg 0.101 and 0.188 respectively) and highlight some interesting phenomena.", }

One of the most significant pieces of ancient Greek literature, the Iliad, is part of humanity{'}s collective cultural heritage. This work aims to provide the scientific community with an emotion-labeled dataset for classical literature and Western mythology in particular. To model the emotions of the poem, we use a multi-variate time series. We also evaluated the dataset by means of two methods. We compare the manual classification against a dictionary-based benchmark as well as employ a state-of-the-art deep learning masked language model that has been tuned using our data. Both evaluations return encouraging results (MSE and MAE Macro Avg 0.101 and 0.188 respectively) and highlight some interesting phenomena.

Deciphering Emotional Landscapes in the Iliad: A Novel French-Annotated Dataset for Emotion Recognition

lrec-main.399

Poster

https://aclanthology.org/2024.lrec-main.400.bib

https://aclanthology.org/2024.lrec-main.400/

@inproceedings{ugan-etal-2024-decm, title = "{DECM}: Evaluating Bilingual {ASR} Performance on a Code-switching/mixing Benchmark", author = "Ugan, Enes Yavuz and Pham, Ngoc-Quan and Waibel, Alexander", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.400", pages = "4468--4475", abstract = "Automatic Speech Recognition has made significant progress, but challenges persist. Code-switched (CSW) Speech presents one such challenge, involving the mixing of multiple languages by a speaker. Even when multilingual ASR models are trained, each utterance on its own usually remains monolingual. We introduce an evaluation dataset for German-English CSW, with German as the matrix language and English as the embedded language. The dataset comprises spontaneous speech from diverse domains, enabling realistic CSW evaluation in German-English. It includes splits with varying degrees of CSW to facilitate specialized model analysis. As it is difficult to collect CSW data for all language pairs, the provision of such evaluation data, is crucial for developing and analyzing ASR models capable of generalizing across unseen pairs. Detailed data statistics are presented, and state-of-the-art (SOTA) multilingual models are evaluated showing challanges of CSW speech.", }

Automatic Speech Recognition has made significant progress, but challenges persist. Code-switched (CSW) Speech presents one such challenge, involving the mixing of multiple languages by a speaker. Even when multilingual ASR models are trained, each utterance on its own usually remains monolingual. We introduce an evaluation dataset for German-English CSW, with German as the matrix language and English as the embedded language. The dataset comprises spontaneous speech from diverse domains, enabling realistic CSW evaluation in German-English. It includes splits with varying degrees of CSW to facilitate specialized model analysis. As it is difficult to collect CSW data for all language pairs, the provision of such evaluation data, is crucial for developing and analyzing ASR models capable of generalizing across unseen pairs. Detailed data statistics are presented, and state-of-the-art (SOTA) multilingual models are evaluated showing challanges of CSW speech.

DECM: Evaluating Bilingual ASR Performance on a Code-switching/mixing Benchmark

lrec-main.400

Poster