pwc-archive/papers-with-abstracts huggingface dataset:
The Keystone Role Played by Questions in Debate
Authors: Zlata Kikteva, Kamila Gorska, Wassiliki Siskou, Annette Hautli-Janisz, Chris Reed
Date: 2022-10-01 00:00:00
Proceeding: COLING (CODI, CRAC) 2022 10
Tasks: Management

Building on the recent results of a study into the roles that are played by questions in argumentative dialogue (Hautli-Janisz et al.,2022a), we expand the analysis to investigate a newly released corpus that constitutes the largest extant corpus of closely annotated debate. Questions play a critical role in driving dialogical discourse forward; in combative or critical discursive environments, they not only provide a range of discourse management techniques, they also scaffold the semantic structure of the positions that interlocutors develop. The boundaries, however, between providing substantive answers to questions, merely responding to questions, and evading questions entirely, are fuzzy and the way in which answers, responses and evasions affect the subsequent development of dialogue and argumentation structure are poorly understood. In this paper, we explore how questions have ramifications on the large-scale structure of a debate using as our substrate the BBC television programme Question Time, the foremost topical debate show in the UK. Analysis of the data demonstrates not only that questioning plays a particularly prominent role in such debate, but also that its repercussions can reverberate through a discourse.
Shallow Discourse Parsing for Open Information Extraction and Text Simplification
Authors: Christina Niklaus, André Freitas, Siegfried Handschuh
Date: 2022-10-01 00:00:00
Proceeding: COLING (CODI, CRAC) 2022 10
Tasks: Discourse Parsing, Open Information Extraction, Sentence, Text Simplification

We present a discourse-aware text simplification (TS) approach that recursively splits and rephrases complex English sentences into a semantic hierarchy of simplified sentences. Using a set of linguistically principled transformation patterns, sentences are converted into a hierarchical representation in the form of core sentences and accompanying contexts that are linked via rhetorical relations. As opposed to previously proposed sentence splitting approaches, which commonly do not take into account discourse-level aspects, our TS approach preserves the semantic relationship of the decomposed constituents in the output. A comparative analysis with the annotations contained in RST-DT shows that we capture the contextual hierarchy between the split sentences with a precision of 89% and reach an average precision of 69% for the classification of the rhetorical relations that hold between them. Moreover, an integration into state-of-the-art Open Information Extraction (IE) systems reveals that when applying our TS approach as a pre-processing step, the generated relational tuples are enriched with additional meta information, resulting in a novel lightweight semantic representation for the task of Open IE.
Predicting Political Orientation in News with Latent Discourse Structure to Improve Bias Understanding
Authors: Nicolas Devatine, Philippe Muller, Chloé Braud
Date: 2022-10-01 00:00:00
Proceeding: COLING (CODI, CRAC) 2022 10
Tasks: Articles

With the growing number of information sources, the problem of media bias becomes worrying for a democratic society. This paper explores the task of predicting the political orientation of news articles, with a goal of analyzing how bias is expressed. We demonstrate that integrating rhetorical dimensions via latent structures over sub-sentential discourse units allows for large improvements, with a +7.4 points difference between the base LSTM model and its discourse-based version, and +3 points improvement over the previous BERT-based state-of-the-art model. We also argue that this gives a new relevant handle for analyzing political bias in news articles.
Attention Modulation for Zero-Shot Cross-Domain Dialogue State Tracking
Authors: Mathilde Veron, Olivier Galibert, Guillaume Bernard, Sophie Rosset
Date: 2022-10-01 00:00:00
Proceeding: COLING (CODI, CRAC) 2022 10
Tasks: dialog state tracking, Dialogue State Tracking, Task-Oriented Dialogue Systems

Dialog state tracking (DST) is a core step for task-oriented dialogue systems aiming to track the user’s current goal during a dialogue. Recently a special focus has been put on applying existing DST models to new domains, in other words performing zero-shot cross-domain transfer. While recent state-of-the-art models leverage large pre-trained language models, no work has been made on understanding and improving the results of first developed zero-shot models like SUMBT. In this paper, we thus propose to improve SUMBT zero-shot results on MultiWOZ by using attention modulation during inference. This method improves SUMBT zero-shot results significantly on two domains and does not worsen the initial performance with the great advantage of needing no additional training.
The CODI-CRAC 2022 Shared Task on Anaphora, Bridging, and Discourse Deixis in Dialogue
Authors: Juntao Yu, Sopan Khosla, Ramesh Manuvinakurike, Lori Levin, Vincent Ng, Massimo Poesio, Michael Strube, Carolyn Rosé
Date: 2022-10-01 00:00:00
Proceeding: COLING (CODI, CRAC) 2022 10

The CODI-CRAC 2022 Shared Task on Anaphora Resolution in Dialogues is the second edition of an initiative focused on detecting different types of anaphoric relations in conversations of different kinds. Using five conversational datasets, four of which have been newly annotated with a wide range of anaphoric relations: identity, bridging references and discourse deixis, we defined multiple tasks focusing individually on these key relations. The second edition of the shared task maintained the focus on these relations and used the same datasets as in 2021, but new test data were annotated, the 2021 data were checked, and new subtasks were added. In this paper, we discuss the annotation schemes, the datasets, the evaluation scripts used to assess the system performance on these tasks, and provide a brief summary of the participating systems and the results obtained across 230 runs from three teams, with most submissions achieving significantly better results than our baseline methods.
Pipeline Coreference Resolution Model for Anaphoric Identity in Dialogues
Authors: Damrin Kim, Seongsik Park, Mirae Han, Harksoo Kim
Date: 2022-10-01 00:00:00
Proceeding: COLING (CODI, CRAC) 2022 10
Tasks: coreference-resolution, Coreference Resolution, Task 2

CODI-CRAC 2022 Shared Task in Dialogues consists of three sub-tasks: Sub-task 1 is the resolution of anaphoric identity, sub-task 2 is the resolution of bridging references, and sub-task 3 is the resolution of discourse deixis/abstract anaphora. Anaphora resolution is the task of detecting mentions from input documents and clustering the mentions of the same entity. The end-to-end model proceeds with the pruning of the candidate mention, and the pruning has the possibility of removing the correct mention. Also, the end-to-end anaphora resolution model has high model complexity, which takes a long time to train. Therefore, we proceed with the anaphora resolution as a two-stage pipeline model. In the first mention detection step, the score of the candidate word span is calculated, and the mention is predicted without pruning. In the second anaphora resolution step, the pair of mentions of the anaphora resolution relationship is predicted using the mentions predicted in the mention detection step. We propose a two-stage anaphora resolution pipeline model that reduces model complexity and training time, and maintains similar performance to end-to-end models. As a result of the experiment, the anaphora resolution showed a performance of 68.27% in Light, 48.87% in AMI, 69.06% in Persuasion, and 60.99% on Switchboard. Our final system ranked 3rd on the leaderboard of sub-task 1.
Neural Anaphora Resolution in Dialogue Revisited
Authors: Shengjie Li, Hideo Kobayashi, Vincent Ng
Date: 2022-10-01 00:00:00
Proceeding: COLING (CODI, CRAC) 2022 10
Tasks: All

We present the systems that we developed for all three tracks of the CODI-CRAC 2022 shared task, namely the anaphora resolution track, the bridging resolution track, and the discourse deixis resolution track. Combining an effective encoding of the input using the SpanBERT_{\text{Large}} encoder with an extensive hyperparameter search process, our systems achieved the highest scores in all phases of all three tracks.
Very Low Resource Sentence Alignment: Luhya and Swahili
Authors: Everlyn Chimoto, Bruce Bassett
Date: 2022-10-01 00:00:00
Proceeding: loresmt (COLING) 2022 10
Tasks: Machine Translation, Sentence, Sentence Embedding, Sentence-Embedding, Sentence Embeddings

Language-agnostic sentence embeddings generated by pre-trained models such as LASER and LaBSE are attractive options for mining large datasets to produce parallel corpora for low-resource machine translation. We test LASER and LaBSE in extracting bitext for two related low-resource African languages: Luhya and Swahili. For this work, we created a new parallel set of nearly 8000 Luhya-English sentences which allows a new zero-shot test of LASER and LaBSE. We find that LaBSE significantly outperforms LASER on both languages. Both LASER and LaBSE however perform poorly at zero-shot alignment on Luhya, achieving just 1.5% and 22.0% successful alignments respectively (P@1 score). We fine-tune the embeddings on a small set of parallel Luhya sentences and show significant gains, improving the LaBSE alignment accuracy to 53.3%. Further, restricting the dataset to sentence embedding pairs with cosine similarity above 0.7 yielded alignments with over 85% accuracy.
Multiple Pivot Languages and Strategic Decoder Initialization Helps Neural Machine Translation
Authors: Shivam Mhaskar, Pushpak Bhattacharyya
Date: 2022-10-01 00:00:00
Proceeding: loresmt (COLING) 2022 10
Tasks: Decoder, Machine Translation, Transfer Learning, Translation

In machine translation, a pivot language can be used to assist the source to target translation model. In pivot-based transfer learning, the source to pivot and the pivot to target models are used to improve the performance of the source to target model. This technique works best when both source-pivot and pivot-target are high resource language pairs and the source-target is a low resource language pair. But in some cases, such as Indic languages, the pivot to target language pair is not a high resource one. To overcome this limitation, we use multiple related languages as pivot languages to assist the source to target model. We show that using multiple pivot languages gives 2.03 BLEU and 3.05 chrF score improvement over the baseline model. We show that strategic decoder initialization while performing pivot-based transfer learning with multiple pivot languages gives a 3.67 BLEU and 5.94 chrF score improvement over the baseline model.
Known Words Will Do: Unknown Concept Translation via Lexical Relations
Authors: Winston Wu, David Yarowsky
Date: 2022-10-01 00:00:00
Proceeding: loresmt (COLING) 2022 10
Tasks: Translation

Translating into low-resource languages is challenging due to the scarcity of training data. In this paper, we propose a probabilistic lexical translation method that bridges through lexical relations including synonyms, hypernyms, hyponyms, and co-hyponyms. This method, which only requires a dictionary like Wiktionary and a lexical database like WordNet, enables the translation of unknown vocabulary into low-resource languages for which we may only know the translation of a related concept. Experiments on translating a core vocabulary set into 472 languages, most of them low-resource, show the effectiveness of our approach.
The Only Chance to Understand: Machine Translation of the Severely Endangered Low-resource Languages of Eurasia
Authors: Anna Mosolova, Kamel Smaili
Date: 2022-10-01 00:00:00
Proceeding: loresmt (COLING) 2022 10
Tasks: Data Augmentation, Language Modeling, Language Modelling, Large Language Model, Machine Translation, Translation

Numerous machine translation systems have been proposed since the appearance of this task. Nowadays, new large language model-based algorithms show results that sometimes overcome human ones on the rich-resource languages. Nevertheless, it is still not the case for the low-resource languages, for which all these algorithms did not show equally impressive results. In this work, we want to compare 3 generations of machine translation models on 7 low-resource languages and make a step further by proposing a new way of automatic parallel data augmentation using the state-of-the-art generative model.
Augmented Bio-SBERT: Improving Performance for Pairwise Sentence Tasks in Bio-medical Domain
Authors: Sonam Pankaj, Amit Gautam
Date: 2022-10-01 00:00:00
Proceeding: loresmt (COLING) 2022 10
Tasks: Data Augmentation, Sentence

One of the modern challenges in AI is the access to high-quality and annotated data, especially in NLP; that is why augmentation is gaining importance. In computer vision, where image data augmentation is standard, text data augmentation in NLP is complex due to the high complexity of language. Moreover, we have seen the advantages of augmentation where there are fewer data available, which can significantly improve the model’s accuracy and performance. We have implemented Augmentation in Pairwise sentence scoring in the biomedical domain. By experimenting with our approach to downstream tasks on biomedical data, we have looked into the solution to improve Bi-encoders’ sentence transformer performance using an augmented dataset generated by cross-encoders fine-tuned on Biosses and MedNLI on the pre-trained Bio-BERT model. It has significantly improved the results with respect to the model only trained on Gold data for the respective tasks.
Machine Translation for a Very Low-Resource Language - Layer Freezing Approach on Transfer Learning
Authors: Amartya Chowdhury, Deepak K. T., Samudra Vijaya K, S. R. Mahadeva Prasanna
Date: 2022-10-01 00:00:00
Proceeding: loresmt (COLING) 2022 10
Tasks: Decoder, Machine Translation, Transfer Learning

This paper presents the implementation of Machine Translation (MT) between Lambani, a low-resource Indian tribal language, and English, a high-resource universal language. Lambani is spoken by nomadic tribes of the Indian state of Karnataka and there are similarities between Lambani and various other Indian languages. To implement the English-Lambani MT system, we followed the transfer learning approach with English-Kannada as the parent MT model. The implementation and performance of the English-Lambani MT system are discussed in this paper. Since Lambani has been influenced by various other languages, we explored the possibility of getting better MT performance by using parent models associated with related Indian languages. Specifically, we experimented with English-Gujarati and English-Marathi as additional parent models. We compare the performance of three different English-Lambani MT systems derived from three parent language models, and the observations are presented in the paper. Additionally, we will also explore the effect of freezing the encoder layer and decoder layer and the change in performance from both of them.
HFT: High Frequency Tokens for Low-Resource NMT
Authors: Edoardo Signoroni, Pavel Rychlý
Date: 2022-10-01 00:00:00
Proceeding: loresmt (COLING) 2022 10
Tasks: Low Resource NMT, Machine Translation, NMT, Segmentation, Vocal Bursts Intensity Prediction

Tokenization has been shown to impact the quality of downstream tasks, such as Neural Machine Translation (NMT), which is susceptible to out-of-vocabulary words and low frequency training data. Current state-of-the-art algorithms have been helpful in addressing the issues of out-of-vocabulary words, bigger vocabulary sizes and token frequency by implementing subword segmentation. We argue, however, that there is still room for improvement, in particular regarding low-frequency tokens in the training data. In this paper, we present “High Frequency Tokenizer”, or HFT, a new language-independent subword segmentation algorithm that addresses this issue. We also propose a new metric to measure the frequency coverage of a tokenizer’s vocabulary, based on a frequency rank weighted average of the frequency values of its items. We experiment with a diverse set of language corpora, vocabulary sizes, and writing systems and report improvements on both frequency statistics and on the average length of the output. We also observe a positive impact on downstream NMT.
Romanian Language Translation in the RELATE Platform
Authors: Vasile Pais, Maria Mitrofan, Andrei-Marius Avram
Date: 2022-10-01 00:00:00
Proceeding: loresmt (COLING) 2022 10
Tasks: Translation

This paper presents the usage of the RELATE platform for translation tasks involving the Romanian language. Using this platform, it is possible to perform text and speech data translations, either for single documents or for entire corpora. Furthermore, the platform was successfully used in international projects to create new resources useful for Romanian language translation.
Translating Spanish into Spanish Sign Language: Combining Rules and Data-driven Approaches
Authors: Luis Chiruzzo, Euan McGill, Santiago Egea-Gómez, Horacio Saggion
Date: 2022-10-01 00:00:00
Proceeding: loresmt (COLING) 2022 10
Tasks: Machine Translation, NMT, Translation

This paper presents a series of experiments on translating between spoken Spanish and Spanish Sign Language glosses (LSE), including enriching Neural Machine Translation (NMT) systems with linguistic features, and creating synthetic data to pretrain and later on finetune a neural translation model. We found evidence that pretraining over a large corpus of LSE synthetic data aligned to Spanish sentences could markedly improve the performance of the translation models.
Benefiting from Language Similarity in the Multilingual MT Training: Case Study of Indonesian and Malaysian
Authors: Alberto Poncelas, Johanes Effendi
Date: 2022-10-01 00:00:00
Proceeding: loresmt (COLING) 2022 10
Tasks: Machine Translation, Translation

The development of machine translation (MT) has been successful in breaking the language barrier of the world’s top 10-20 languages. However, for the rest of it, delivering an acceptable translation quality is still a challenge due to the limited resource. To tackle this problem, most studies focus on augmenting data while overlooking the fact that we can borrow high-quality natural data from the closely-related language. In this work, we propose an MT model training strategy by increasing the language directions as a means of augmentation in a multilingual setting. Our experiment result using Indonesian and Malaysian on the state-of-the-art MT model showcases the effectiveness and robustness of our method.
Exploring Word Alignment towards an Efficient Sentence Aligner for Filipino and Cebuano Languages
Authors: Jenn Leana Fernandez, Kristine Mae M. Adlaon
Date: 2022-10-01 00:00:00
Proceeding: loresmt (COLING) 2022 10
Tasks: Machine Translation, Sentence, Translation, Word Alignment

Building a robust machine translation (MT) system requires a large amount of parallel corpus which is an expensive resource for low-resourced languages. The two major languages being spoken in the Philippines which are Filipino and Cebuano have an abundance in monolingual data that this study took advantage of attempting to find the best way to automatically generate parallel corpus out from monolingual corpora through the use of bitext alignment. Byte-pair encoding was applied in an attempt to optimize the alignment of the source and target texts. Results have shown that alignment was best achieved without segmenting the tokens. Itermax alignment score is best for short-length sentences and match or argmax alignment score are best for long-length sentences.
Aligning Word Vectors on Low-Resource Languages with Wiktionary
Authors: Mike Izbicki
Date: 2022-10-01 00:00:00
Proceeding: loresmt (COLING) 2022 10
Tasks: Word Embeddings

Aligned word embeddings have become a popular technique for low-resource natural language processing. Most existing evaluation datasets are generated automatically from machine translations systems, so they have many errors and exist only for high-resource languages. We introduce the Wiktionary bilingual lexicon collection, which provides high-quality human annotated translations for words in 298 languages to English. We use these lexicons to train and evaluate the largest published collection of aligned word embeddings on 157 different languages. All of our code and data is publicly available at https://github.com/mikeizbicki/wiktionary_bli.
Overview of the Third Workshop on Scholarly Document Processing
Authors: Arman Cohan, Guy Feigenblat, Dayne Freitag, Tirthankar Ghosal, Drahomira Herrmannova, Petr Knoth, Kyle Lo, Philipp Mayr, Michal Shmueli-Scheuer, Anita de Waard, Lucy Lu Wang
Date: 2022-10-01 00:00:00
Proceeding: sdp (COLING) 2022 10
Tasks: Document Summarization, Graph Generation, Information Retrieval, Multi-Document Summarization, Retrieval, Scientific Document Summarization

With the ever-increasing pace of research and high volume of scholarly communication, scholars face a daunting task. Not only must they keep up with the growing literature in their own and related fields, scholars increasingly also need to rebut pseudo-science and disinformation. These needs have motivated an increasing focus on computational methods for enhancing search, summarization, and analysis of scholarly documents. However, the various strands of research on scholarly document processing remain fragmented. To reach out to the broader NLP and AI/ML community, pool distributed efforts in this area, and enable shared access to published research, we held the 3rd Workshop on Scholarly Document Processing (SDP) at COLING as a hybrid event (https://sdproc.org/2022/). The SDP workshop consisted of a research track, three invited talks and five Shared Tasks: 1) MSLR22: Multi-Document Summarization for Literature Reviews, 2) DAGPap22: Detecting automatically generated scientific papers, 3) SV-Ident 2022: Survey Variable Identification in Social Science Publications, 4) SKGG: Scholarly Knowledge Graph Generation, 5) MuP 2022: Multi Perspective Scientific Document Summarization. The program was geared towards NLP, information retrieval, and data mining for scholarly documents, with an emphasis on identifying and providing solutions to open challenges.
Finding Scientific Topics in Continuously Growing Text Corpora
Authors: André Bittermann, Jonas Rieger
Date: 2022-10-01 00:00:00
Proceeding: sdp (COLING) 2022 10
Tasks: Time Series, Time Series Analysis

The ever growing amount of research publications demands computational assistance for everyone trying to keep track with scientific processes. Topic modeling has become a popular approach for finding scientific topics in static collections of research papers. However, the reality of continuously growing corpora of scholarly documents poses a major challenge for traditional approaches. We introduce RollingLDA for an ongoing monitoring of research topics, which offers the possibility of sequential modeling of dynamically growing corpora with time consistency of time series resulting from the modeled texts. We evaluate its capability to detect research topics and present a Shiny App as an easy-to-use interface. In addition, we illustrate usage scenarios for different user groups such as researchers, students, journalists, or policy-makers.