ymoslem/acl-abstracts huggingface dataset:
Semantic Specialization for Knowledge-based Word Sense Disambiguation (2023)

A promising approach for knowledge-based Word Sense Disambiguation (WSD) is to select the sense whose contextualized embeddings computed for its definition sentence are closest to those computed for a target word in a given sentence. This approach relies on the similarity of the sense and context embeddings computed by a pre-trained language model. We propose a semantic specialization for WSD where contextualized embeddings are adapted to the WSD task using solely lexical knowledge. The key idea is, for a given sense, to bring semantically related senses and contexts closer and send different/unrelated senses farther away. We realize this idea as the joint optimization of the Attract-Repel objective for sense pairs and the self-training objective for context-sense pairs while controlling deviations from the original embeddings. The proposed method outperformed previous studies that adapt contextualized embeddings. It achieved state-of-the-art performance on knowledge-based WSD when combined with the reranking heuristic that uses the sense inventory. We found that the similarity characteristics of specialized embeddings conform to the key idea. We also found that the (dis)similarity of embeddings between the related/different/unrelated senses correlates well with the performance of WSD.
Concept-based Persona Expansion for Improving Diversity of Persona-Grounded Dialogue (2023)

A persona-grounded dialogue model aims to improve the quality of responses to promote user engagement. However, because the given personas are mostly short and limited to only a few informative words, it is challenging to utilize them to generate diverse responses. To tackle this problem, we propose a novel persona expansion framework, Concept-based Persona eXpansion (CPX). CPX takes the original persona as input and generates expanded personas that contain conceptually rich content. We constitute CPX with two task modules: 1) Concept Extractor and 2) Sentence Generator. To train these modules, we exploit the duality of two tasks with a commonsense dataset consisting of a concept set and the corresponding sentences which contain the given concepts. Extensive experiments on persona expansion and response generation show that our work sufficiently contributes to improving the quality of responses in diversity and richness.
RPTCS: A Reinforced Persona-aware Topic-guiding Conversational System (2023)

Although there has been a plethora of work on open-domain conversational systems, most of the systems lack the mechanism of controlling the concept transitions in a dialogue. For activities like switching from casual chit-chat to task-oriented conversation, an agent with the ability to manage the flow of concepts in a conversation might be helpful. The user would find the dialogue more engaging and be more receptive to such transitions if these concept transitions were made while taking into account the user`s persona. Focusing on persona-aware concept transitions, we propose a Reinforced Persona-aware Topic-guiding Conversational System (RPTCS). Due to the lack of a persona-aware topic transition dataset, we propose a novel conversation dataset creation mechanism in which the conversational agent leads the discourse to drift to a set of target concepts depending on the persona of the speaker and the context of the conversation. To avoid scarcely available expensive human resource, the entire data-creation process is mostly automatic with human-in-loop only for quality checks. This created conversational dataset named PTCD is used to develop the RPTCS in two steps. First, a maximum likelihood estimation loss-based conversational model is trained on PTCD. Then this trained model is fine-tuned in a Reinforcement Learning (RL) framework by employing novel reward functions to assure persona, topic, and context consistency with non-repetitiveness in generated responses. Our experimental results demonstrate the strength of the proposed system with respect to strong baselines.
What Did You Learn To Hate? A Topic-Oriented Analysis of Generalization in Hate Speech Detection (2023)

Hate speech has unfortunately become a significant phenomenon on social media platforms, and it can cover various topics (misogyny, sexism, racism, xenophobia, etc.) and targets (e.g., black people, women). Various hate speech detection datasets have been proposed, some annotated for specific topics, and others for hateful speech in general. In either case, they often employ different annotation guidelines, which can lead to inconsistencies, even in datasets focusing on the same topics. This can cause issues in models trying to generalize across more data and more topics in order to improve detection accuracy. In this paper, we propose, for the first time, a topic-oriented approach to study generalization across popular hate speech datasets. We first perform a comparative analysis of the performances of Transformer-based models in capturing topic-generic and topic-specific knowledge when trained on different datasets. We then propose a novel, simple yet effective approach to study more precisely which topics are best captured in implicit manifestations of hate, showing that selecting combinations of datasets with better out-of-domain topical coverage improves the reliability of automatic hate speech detection.
End-to-end Case-Based Reasoning for Commonsense Knowledge Base Completion (2023)

Pretrained language models have been shown to store knowledge in their parameters and have achieved reasonable performance in commonsense knowledge base completion (CKBC) tasks. However, CKBC is knowledge-intensive and it is reported that pretrained language models' performance in knowledge-intensive tasks are limited because of their incapability of accessing and manipulating knowledge. As a result, we hypothesize that providing retrieved passages that contain relevant knowledge as additional input to the CKBC task will improve performance. In particular, we draw insights from Case-Based Reasoning (CBR) {--} which aims to solve a new problem by reasoning with retrieved relevant cases, and investigate the direct application of it to CKBC. On two benchmark datasets, we demonstrate through automatic and human evaluations that our End-to-end Case-Based Reasoning Framework (ECBRF) generates more valid, informative, and novel knowledge than the state-of-the-art COMET model for CKBC in both the fully supervised and few-shot settings. We provide insights on why previous retrieval-based methods only achieve merely the same performance with COMET. From the perspective of CBR, our framework addresses a fundamental question on whether CBR methodology can be utilized to improve deep learning models.
Exploring Segmentation Approaches for Neural Machine Translation of Code-Switched Egyptian Arabic-English Text (2023)

Data sparsity is one of the main challenges posed by code-switching (CS), which is further exacerbated in the case of morphologically rich languages. For the task of machine translation (MT), morphological segmentation has proven successful in alleviating data sparsity in monolingual contexts; however, it has not been investigated for CS settings. In this paper, we study the effectiveness of different segmentation approaches on MT performance, covering morphology-based and frequency-based segmentation techniques. We experiment on MT from code-switched Arabic-English to English. We provide detailed analysis, examining a variety of conditions, such as data size and sentences with different degrees of CS. Empirical results show that morphology-aware segmenters perform the best in segmentation tasks but under-perform in MT. Nevertheless, we find that the choice of the segmentation setup to use for MT is highly dependent on the data size. For extreme low-resource scenarios, a combination of frequency and morphology-based segmentations is shown to perform the best. For more resourced settings, such a combination does not bring significant improvements over the use of frequency-based segmentation.
Identifying the limits of transformers when performing model-checking with natural language (2023)

Can transformers learn to comprehend logical semantics in natural language? Although many strands of work on natural language inference have focussed on transformer models' ability to perform reasoning on text, the above question has not been answered adequately. This is primarily because the logical problems that have been studied in the context of natural language inference have their computational complexity vary with the logical and grammatical constructs within the sentences. As such, it is difficult to access whether the difference in accuracy is due to logical semantics or the difference in computational complexity. A problem that is much suited to address this issue is that of the model-checking problem, whose computational complexity remains constant (for fragments derived from first-order logic). However, the model-checking problem remains untouched in natural language inference research. Thus, we investigated the problem of model-checking with natural language to adequately answer the question of how the logical semantics of natural language affects transformers' performance. Our results imply that the language fragment has a significant impact on the performance of transformer models. Furthermore, we hypothesise that a transformer model can at least partially understand the logical semantics in natural language but can not completely learn the rules governing the model-checking algorithm.
Improving the Generalizability of Collaborative Dialogue Analysis With Multi-Feature Embeddings (2023)

Conflict prediction in communication is integral to the design of virtual agents that support successful teamwork by providing timely assistance. The aim of our research is to analyze discourse to predict collaboration success. Unfortunately, resource scarcity is a problem that teamwork researchers commonly face since it is hard to gather a large number of training examples. To alleviate this problem, this paper introduces a multi-feature embedding (MFeEmb) that improves the generalizability of conflict prediction models trained on dialogue sequences. MFeEmb leverages textual, structural, and semantic information from the dialogues by incorporating lexical, dialogue acts, and sentiment features. The use of dialogue acts and sentiment features reduces performance loss from natural distribution shifts caused mainly by changes in vocabulary. This paper demonstrates the performance of MFeEmb on domain adaptation problems in which the model is trained on discourse from one task domain and applied to predict team performance in a different domain. The generalizability of MFeEmb is quantified using the similarity measure proposed by Bontonou et al. (2021). Our results show that MFeEmb serves as an excellent domain-agnostic representation for meta-pretraining a few-shot model on collaborative multiparty dialogues.
MetaQA: Combining Expert Agents for Multi-Skill Question Answering (2023)

The recent explosion of question-answering (QA) datasets and models has increased the interest in the generalization of models across multiple domains and formats by either training on multiple datasets or combining multiple models. Despite the promising results of multi-dataset models, some domains or QA formats may require specific architectures, and thus the adaptability of these models might be limited. In addition, current approaches for combining models disregard cues such as question-answer compatibility. In this work, we propose to combine expert agents with a novel, flexible, and training-efficient architecture that considers questions, answer predictions, and answer-prediction confidence scores to select the best answer among a list of answer predictions. Through quantitative and qualitative experiments, we show that our model i) creates a collaboration between agents that outperforms previous multi-agent and multi-dataset approaches, ii) is highly data-efficient to train, and iii) can be adapted to any QA format. We release our code and a dataset of answer predictions from expert agents for 16 QA datasets to foster future research of multi-agent systems.
BERT Is Not The Count: Learning to Match Mathematical Statements with Proofs (2023)

We introduce a task consisting in matching a proof to a given mathematical statement. The task fits well within current research on Mathematical Information Retrieval and, more generally, mathematical article analysis (Mathematical Sciences, 2014). We present a dataset for the task (the MATcH dataset) consisting of over 180k statement-proof pairs extracted from modern mathematical research articles. We find this dataset highly representative of our task, as it consists of relatively new findings useful to mathematicians. We propose a bilinear similarity model and two decoding methods to match statements to proofs effectively. While the first decoding method matches a proof to a statement without being aware of other statements or proofs, the second method treats the task as a global matching problem. Through a symbol replacement procedure, we analyze the {\textquotedblleft}insights{\textquotedblright} that pre-trained language models have in such mathematical article analysis and show that while these models perform well on this task with the best performing mean reciprocal rank of 73.7, they follow a relatively shallow symbolic analysis and matching to achieve that performance.
Lessons Learned from a Citizen Science Project for Natural Language Processing (2023)

Many Natural Language Processing (NLP) systems use annotated corpora for training and evaluation. However, labeled data is often costly to obtain and scaling annotation projects is difficult, which is why annotation tasks are often outsourced to paid crowdworkers. Citizen Science is an alternative to crowdsourcing that is relatively unexplored in the context of NLP. To investigate whether and how well Citizen Science can be applied in this setting, we conduct an exploratory study into engaging different groups of volunteers in Citizen Science for NLP by re-annotating parts of a pre-existing crowdsourced dataset. Our results show that this can yield high-quality annotations and at- tract motivated volunteers, but also requires considering factors such as scalability, participation over time, and legal and ethical issues. We summarize lessons learned in the form of guidelines and provide our code and data to aid future work on Citizen Science.
Contrastive Learning with Keyword-based Data Augmentation for Code Search and Code Question Answering (2023)

The semantic code search is to find code snippets from the collection of candidate code snippets with respect to a user query that describes functionality. Recent work on code search proposes data augmentation of queries for contrastive learning. This data augmentation approach modifies random words in queries. When a user web query for searching code snippet is too brief, the important word that represents the search intent of the query could be undesirably modified. A code snippet has informative components such as function name and documentation that describe its functionality. We propose to utilize these code components to identify important words and preserve them in the data augmentation step. We present KeyDAC (Keyword-based Data Augmentation for Contrastive learning) that identifies important words for code search from queries and code components based on term matching. KeyDAC augments query-code pairs while preserving keywords, and then leverages generated training instances for contrastive learning. We use KeyDAC to fine-tune various pre-trained language models and evaluate the performance of code search and code question answering via CoSQA and WebQueryTest. The experimental results confirm that KeyDAC substantially outperforms the current state-of-the-art performance, and achieves the new state-of-the-arts for both tasks.
Large Scale Multi-Lingual Multi-Modal Summarization Dataset (2023)

Significant developments in techniques such as encoder-decoder models have enabled us to represent information comprising multiple modalities. This information can further enhance many downstream tasks in the field of information retrieval and natural language processing; however, improvements in multi-modal techniques and their performance evaluation require large-scale multi-modal data which offers sufficient diversity. Multi-lingual modeling for a variety of tasks like multi-modal summarization, text generation, and translation leverages information derived from high-quality multi-lingual annotated data. In this work, we present the current largest multi-lingual multi-modal summarization dataset (M3LS), and it consists of over a million instances of document-image pairs along with a professionally annotated multi-modal summary for each pair. It is derived from news articles published by British Broadcasting Corporation(BBC) over a decade and spans 20 languages, targeting diversity across five language roots, it is also the largest summarization dataset for 13 languages and consists of cross-lingual summarization data for 2 languages. We formally define the multi-lingual multi-modal summarization task utilizing our dataset and report baseline scores from various state-of-the-art summarization techniques in a multi-lingual setting. We also compare it with many similar datasets to analyze the uniqueness and difficulty of M3LS. The dataset and code used in this work are made available at {\textquotedblleft}\url{https://github.com/anubhav-jangra/M3LS}{\textquotedblright}.
External Knowledge Acquisition for End-to-End Document-Oriented Dialog Systems (2023)

End-to-end neural models for conversational AI often assume that a response can be generated by considering only the knowledge acquired by the model during training. Document-oriented conversational models make a similar assumption by conditioning the input on the document and assuming that any other knowledge is captured in the model`s weights. However, a conversation may refer to external knowledge sources. In this work, we present EKo-Doc, an architecture for document-oriented conversations with access to external knowledge: we assume that a conversation is centered around a topic document and that external knowledge is needed to produce responses. EKo-Doc includes a dense passage retriever, a re-ranker, and a response generation model. We train the model end-to-end by using silver labels for the retrieval and re-ranking components that we automatically acquire from the attention signals of the response generation model. We demonstrate with automatic and human evaluations that incorporating external knowledge improves response generation in document-oriented conversations. Our architecture achieves new state-of-the-art results on the Wizard of Wikipedia dataset, outperforming a competitive baseline by 10.3{\%} in Recall@1 and 7.4{\%} in ROUGE-L.
In-Depth Look at Word Filling Societal Bias Measures (2023)

Many measures of societal bias in language models have been proposed in recent years. A popular approach is to use a set of word filling prompts to evaluate the behavior of the language models. In this work, we analyze the validity of two such measures {--} StereoSet and CrowS-Pairs. We show that these measures produce unexpected and illogical results when appropriate control group samples are constructed. Based on this, we believe that they are problematic and using them in the future should be reconsidered. We propose a way forward with an improved testing protocol. Finally, we also introduce a new gender bias dataset for Slovak.
Retrieval-augmented Image Captioning (2023)

Inspired by retrieval-augmented language generation and pretrained Vision and Language (V{\&}L) encoders, we present a new approach to image captioning that generates sentences given the input image and a set of captions retrieved from a datastore, as opposed to the image alone. The encoder in our model jointly processes the image and retrieved captions using a pretrained V{\&}L BERT, while the decoder attends to the multimodal encoder representations, benefiting from the extra textual evidence from the retrieved captions. Experimental results on the COCO dataset show that image captioning can be effectively formulated from this new perspective. Our model, named EXTRA, benefits from using captions retrieved from the training dataset, and it can also benefit from using an external dataset without the need for retraining. Ablation studies show that retrieving a sufficient number of captions (e.g., k=5) can improve captioning quality. Our work contributes towards using pretrained V{\&}L encoders for generative tasks, instead of standard classification tasks.
Automatic Evaluation and Analysis of Idioms in Neural Machine Translation (2023)

A major open problem in neural machine translation (NMT) is the translation of idiomatic expressions, such as {\textquotedblleft}under the weather{\textquotedblright}. The meaning of these expressions is not composed by the meaning of their constituent words, and NMT models tend to translate them literally (i.e., word-by-word), which leads to confusing and nonsensical translations. Research on idioms in NMT is limited and obstructed by the absence of automatic methods for quantifying these errors. In this work, first, we propose a novel metric for automatically measuring the frequency of literal translation errors without human involvement. Equipped with this metric, we present controlled translation experiments with models trained in different conditions (with/without the test-set idioms) and across a wide range of (global and targeted) metrics and test sets. We explore the role of monolingual pretraining and find that it yields substantial targeted improvements, even without observing any translation examples of the test-set idioms. In our analysis, we probe the role of idiom context. We find that the randomly initialized models are more local or {\textquotedblleft}myopic{\textquotedblright} as they are relatively unaffected by variations of the idiom context, unlike the pretrained ones.
Representation biases in sentence transformers (2023)

Variants of the BERT architecture specialised for producing full-sentence representations often achieve better performance on downstream tasks than sentence embeddings extracted from vanilla BERT. However, there is still little understanding of what properties of inputs determine the properties of such representations. In this study, we construct several sets of sentences with pre-defined lexical and syntactic structures and show that SOTA sentence transformers have a strong nominal-participant-set bias: cosine similarities between pairs of sentences are more strongly determined by the overlap in the set of their noun participants than by having the same predicates, lengthy nominal modifiers, or adjuncts. At the same time, the precise syntactic-thematic functions of the participants are largely irrelevant.
AbLit: A Resource for Analyzing and Generating Abridged Versions of English Literature (2023)

Creating an abridged version of a text involves shortening it while maintaining its linguistic qualities. In this paper, we examine this task from an NLP perspective for the first time. We present a new resource, AbLit, which is derived from abridged versions of English literature books. The dataset captures passage-level alignments between the original and abridged texts. We characterize the linguistic relations of these alignments, and create automated models to predict these relations as well as to generate abridgements for new texts. Our findings establish abridgement as a challenging task, motivating future resources and research. The dataset is available at github.com/roemmele/AbLit.