ymoslem/acl-abstracts huggingface dataset:
ZELDA: A Comprehensive Benchmark for Supervised Entity Disambiguation (2023)

Entity disambiguation (ED) is the task of disambiguating named entity mentions in text to unique entries in a knowledge base. Due to its industrial relevance, as well as current progress in leveraging pre-trained language models, a multitude of ED approaches have been proposed in recent years. However, we observe a severe lack of uniformity across experimental setups in current ED work,rendering a direct comparison of approaches based solely on reported numbers impossible: Current approaches widely differ in the data set used to train, the size of the covered entity vocabulary, and the usage of additional signals such as candidate lists. To address this issue, we present ZELDA , a novel entity disambiguation benchmark that includes a unified training data set, entity vocabulary, candidate lists, as well as challenging evaluation splits covering 8 different domains. We illustrate its design and construction, and present experiments in which we train and compare current state-of-the-art approaches on our benchmark. To encourage greater direct comparability in the entity disambiguation domain, we make our benchmark publicly available to the research community.
GLADIS: A General and Large Acronym Disambiguation Benchmark (2023)

Acronym Disambiguation (AD) is crucial for natural language understanding on various sources, including biomedical reports, scientific papers, and search engine queries. However, existing acronym disambiguationbenchmarks and tools are limited to specific domains, and the size of prior benchmarks is rather small. To accelerate the research on acronym disambiguation, we construct a new benchmark with three components: (1) a much larger acronym dictionary with 1.5M acronyms and 6.4M long forms; (2) a pre-training corpus with 160 million sentences;(3) three datasets that cover thegeneral, scientific, and biomedical domains. We then pre-train a language model, \textit{AcroBERT}, on our constructed corpus for general acronym disambiguation, and show the challenges and values of our new benchmark.
Probing Cross-Lingual Lexical Knowledge from Multilingual Sentence Encoders (2023)

Pretrained multilingual language models (LMs) can be successfully transformed into multilingual sentence encoders (SEs; e.g., LaBSE, xMPNet) via additional fine-tuning or model distillation with parallel data. However, it remains unclear how to best leverage them to represent sub-sentence lexical items (i.e., words and phrases) in cross-lingual lexical tasks. In this work, we probe SEs for the amount of cross-lingual lexical knowledge stored in their parameters, and compare them against the original multilingual LMs. We also devise a simple yet efficient method for exposing the cross-lingual lexical knowledge by means of additional fine-tuning through inexpensive contrastive learning that requires only a small amount of word translation pairs. Using bilingual lexical induction (BLI), cross-lingual lexical semantic similarity, and cross-lingual entity linking as lexical probing tasks, we report substantial gains on standard benchmarks (e.g., +10 Precision@1 points in BLI). The results indicate that the SEs such as LaBSE can be {\textquoteleft}rewired' into effective cross-lingual lexical encoders via the contrastive learning procedure, and that it is possible to expose more cross-lingual lexical knowledge compared to using them as off-the-shelf SEs. This way, we also provide an effective tool for harnessing {\textquoteleft}covert' multilingual lexical knowledge hidden in multilingual sentence encoders.
Pento-DIARef: A Diagnostic Dataset for Learning the Incremental Algorithm for Referring Expression Generation from Examples (2023)

NLP tasks are typically defined extensionally through datasets containing example instantiations (e.g., pairs of image {\_}i{\_} and text {\_}t{\_}), but motivated intensionally through capabilities invoked in verbal descriptions of the task (e.g., {\textquotedblleft}{\_}t{\_} is a description of {\_}i{\_}, for which the content of {\_}i{\_} needs to be recognised and understood{\textquotedblright}).We present Pento-DIARef, a diagnostic dataset in a visual domain of puzzle pieces where referring expressions are generated by a well-known symbolic algorithm (the {\textquotedblleft}Incremental Algorithm{\textquotedblright}),which itself is motivated by appeal to a hypothesised capability (eliminating distractors through application of Gricean maxims). Our question then is whether the extensional description (the dataset) is sufficient for a neural model to pick up the underlying regularity and exhibit this capability given the simple task definition of producing expressions from visual inputs. We find that a model supported by a vision detection step and a targeted data generation scheme achieves an almost perfect BLEU@1 score and sentence accuracy, whereas simpler baselines do not.
Mitigating Exposure Bias in Grammatical Error Correction with Data Augmentation and Reweighting (2023)

The most popular approach in grammatical error correction (GEC) is based on sequence-to-sequence (seq2seq) models. Similar to other autoregressive generation tasks, seq2seq GEC also faces the exposure bias problem, i.e., the context tokens are drawn from different distributions during training and testing, caused by the teacher forcing mechanism. In this paper, we propose a novel data manipulation approach to overcome this problem, which includes a data augmentation method during training to mimic the decoder input at inference time, and a data reweighting method to automatically balance the importance of each kind of augmented samples. Experimental results on benchmark GEC datasets show that our method achieves significant improvements compared to prior approaches.
Plausible May Not Be Faithful: Probing Object Hallucination in Vision-Language Pre-training (2023)

Large-scale vision-language pre-trained (VLP) models are prone to hallucinate non-existent visual objects when generating text based on visual information. In this paper, we systematically study the object hallucination problem from three aspects. First, we examine recent state-of-the-art VLP models, showing that they still hallucinate frequently and models achieving better scores on standard metrics (e.g., CIDEr) could be more unfaithful. Second, we investigate how different types of image encoding in VLP influence hallucination, including region-based, grid-based, and patch-based. Surprisingly, we find that patch-based features perform the best and smaller patch resolution yields a non-trivial reduction in object hallucination. Third, we decouple various VLP objectives and demonstrate that token-level image-text alignment and controlled generation are crucial to reducing hallucination. Based on that, we propose a simple yet effective VLP loss named ObjMLM to further mitigate object hallucination. Results show that it reduces object hallucination by up to 17.4{\%} when tested on two benchmarks (COCO Caption for in-domain and NoCaps for out-of-domain evaluation).
Characterizing the Entities in Harmful Memes: Who is the Hero, the Villain, the Victim? (2023)

Memes can sway people`s opinions over social media as they combine visual and textual information in an easy-to-consume manner. Since memes instantly turn viral, it becomes crucial to infer their intent and potentially associated harmfulness to take timely measures as needed. A common problem associated with meme comprehension lies in detecting the entities referenced and characterizing the role of each of these entities. Here, we aim to understand whether the meme glorifies, vilifies, or victimizes each entity it refers to. To this end, we address the task of role identification of entities in harmful memes, i.e., detecting who is the {\textquoteleft}hero', the {\textquoteleft}villain', and the {\textquoteleft}victim' in the meme, if any. We utilize HVVMemes {--} a memes dataset on US Politics and Covid-19 memes, released recently as part of the CONSTRAINT@ACL-2022 shared-task. It contains memes, entities referenced, and their associated roles: hero, villain, victim, and other. We further design VECTOR (Visual-semantic role dEteCToR), a robust multi-modal framework for the task, which integrates entity-based contextual information in the multi-modal representation and compare it to several standard unimodal (text-only or image-only) or multi-modal (image+text) models. Our experimental results show that our proposed model achieves an improvement of 4{\%} over the best baseline and 1{\%} over the best competing stand-alone submission from the shared-task. Besides divulging an extensive experimental setup with comparative analyses, we finally highlight the challenges encountered in addressing the complex task of semantic role labeling within memes.
Systematic Investigation of Strategies Tailored for Low-Resource Settings for Low-Resource Dependency Parsing (2023)

In this work, we focus on low-resource dependency parsing for multiple languages. Several strategies are tailored to enhance performance in low-resource scenarios. While these are well-known to the community, it is not trivial to select the best-performing combination of these strategies for a low-resource language that we are interested in, and not much attention has been given to measuring the efficacy of these strategies. We experiment with 5 low-resource strategies for our ensembled approach on 7 Universal Dependency (UD) low-resource languages. Our exhaustive experimentation on these languages supports the effective improvements for languages not covered in pretrained models. We show a successful application of the ensembled system on a truly low-resource language Sanskrit. The code and data are available at: \url{https://github.com/Jivnesh/SanDP}
Compositional Generalisation with Structured Reordering and Fertility Layers (2023)

Seq2seq models have been shown to struggle with compositional generalisation, i.e. generalising to new and potentially more complex structures than seen during training. Taking inspiration from grammar-based models that excel at compositional generalisation, we present a flexible end-to-end differentiable neural model that composes two structural operations: a fertility step, which we introduce in this work, and a reordering step based on previous work (Wang et al., 2021). To ensure differentiability, we use the expected value of each step, which we compute using dynamic programming. Our model outperforms seq2seq models by a wide margin on challenging compositional splits of realistic semantic parsing tasks that require generalisation to longer examples. It also compares favourably to other models targeting compositional generalisation.
Investigating Multi-source Active Learning for Natural Language Inference (2023)

In recent years, active learning has been successfully applied to an array of NLP tasks. However, prior work often assumes that training and test data are drawn from the same distribution. This is problematic, as in real-life settings data may stem from several sources of varying relevance and quality. We show that four popular active learning schemes fail to outperform random selection when applied to unlabelled pools comprised of multiple data sources on the task of natural language inference. We reveal that uncertainty-based strategies perform poorly due to the acquisition of collective outliers, i.e., hard-to-learn instances that hamper learning and generalisation. When outliers are removed, strategies are found to recover and outperform random baselines. In further analysis, we find that collective outliers vary in form between sources, and show that hard-to-learn data is not always categorically harmful. Lastly, we leverage dataset cartography to introduce difficulty-stratified testing and find that different strategies are affected differently by example learnability and difficulty.
Towards a Unified Multi-Domain Multilingual Named Entity Recognition Model (2023)

Named Entity Recognition is a key Natural Language Processing task whose performance is sensitive to choice of genre and language. A unified NER model across multiple genres and languages is more practical and efficient by leveraging commonalities across genres or languages. In this paper, we propose a novel setup for NER which includes multi-domain and multilingual training and evaluation across 13 domains and 4 languages. We explore a range of approaches to building a unified model using domain and language adaptation techniques. Our experiments highlight multiple nuances to consider while building a unified model, including that naive data pooling fails to obtain good performance, that domain-specific adaptations are more important than language-specific ones and that including domain-specific adaptations in a unified model nears the performance of training multiple dedicated monolingual models at a fraction of their parameter count.
Do Neural Topic Models Really Need Dropout? Analysis of the Effect of Dropout in Topic Modeling (2023)

Dropout is a widely used regularization trick to resolve the overfitting issue in large feedforward neural networks trained on a small dataset, which performs poorly on the held-out test subset. Although the effectiveness of this regularization trick has been extensively studied for convolutional neural networks, there is a lack of analysis of it for unsupervised models and in particular, VAE-based neural topic models. In this paper, we have analyzed the consequences of dropout in the encoder as well as in the decoder of the VAE architecture in three widely used neural topic models, namely, contextualized topic model (CTM), ProdLDA, and embedded topic model (ETM) using four publicly available datasets. We characterize the dropout effect on these models in terms of the quality and predictive performance of the generated topics.
A Psycholinguistic Analysis of BERT`s Representations of Compounds (2023)

This work studies the semantic representations learned by BERT for compounds, that is, expressions such as sunlight or bodyguard. We build on recent studies that explore semantic information in Transformers at the word level and test whether BERT aligns with human semantic intuitions when dealing with expressions (e.g., sunlight) whose overall meaning depends{---}to a various extent{---}on the semantics of the constituent words (sun, light). We leverage a dataset that includes human judgments on two psycholinguistic measures of compound semantic analysis: lexeme meaning dominance (LMD; quantifying the weight of each constituent toward the compound meaning) and semantic transparency (ST; evaluating the extent to which the compound meaning is recoverable from the constituents' semantics). We show that BERT-based measures moderately align with human intuitions, especially when using contextualized representations, and that LMD is overall more predictable than ST. Contrary to the results reported for {\textquoteleft}standard' words, higher, more contextualized layers are the best at representing compound meaning. These findings shed new light on the abilities of BERT in dealing with fine-grained semantic phenomena. Moreover, they can provide insights into how speakers represent compounds.
Measuring Normative and Descriptive Biases in Language Models Using Census Data (2023)

We investigate in this paper how distributions of occupations with respect to gender is reflected in pre-trained language models. Such distributions are not always aligned to normative ideals, nor do they necessarily reflect a descriptive assessment of reality. In this paper, we introduce an approach for measuring to what degree pre-trained language models are aligned to normative and descriptive occupational distributions. To this end, we use official demographic information about gender{--}occupation distributions provided by the national statistics agencies of France, Norway, United Kingdom, and the United States. We manually generate template-based sentences combining gendered pronouns and nouns with occupations, and subsequently probe a selection of ten language models covering the English, French, and Norwegian languages. The scoring system we introduce in this work is language independent, and can be used on any combination of template-based sentences, occupations, and languages. The approach could also be extended to other dimensions of national census data and other demographic variables.
UDAPTER - Efficient Domain Adaptation Using Adapters (2023)

We propose two methods to make unsupervised domain adaptation (UDA) more parameter efficient using adapters {--} small bottleneck layers interspersed with every layer of the large-scale pre-trained language model (PLM). The first method deconstructs UDA into a two-step process: first by adding a domain adapter to learn domain-invariant information and then by adding a task adapter that uses domain-invariant information to learn task representations in the source domain. The second method jointly learns a supervised classifier while reducing the divergence measure. Compared to strong baselines, our simple methods perform well in natural language inference (MNLI) and the cross-domain sentiment classification task. We even outperform unsupervised domain adaptation methods such as DANN and DSN in sentiment classification, and we are within 0.85{\%} F1 for natural language inference task, by fine-tuning only a fraction of the full model parameters. We release our code at this URL.
Efficient CTC Regularization via Coarse Labels for End-to-End Speech Translation (2023)

For end-to-end speech translation, regularizing the encoder with the Connectionist Temporal Classification (CTC) objective using the source transcript or target translation as labels can greatly improve quality. However, CTC demands an extra prediction layer over the vocabulary space, bringing in non-negligible model parameters and computational overheads, although this layer becomes useless at inference. In this paper, we re-examine the need for genuine vocabulary labels for CTC for regularization and explore strategies to reduce the CTC label space, targeting improved efficiency without quality degradation. We propose coarse labeling for CTC (CoLaCTC), which merges vocabulary labels via simple heuristic rules, such as using truncation, division or modulo (MOD) operations. Despite its simplicity, our experiments on 4 source and 8 target languages show that CoLaCTC with MOD particularly can compress the label space aggressively to 256 and even further, gaining training efficiency (1.18{\texttimes} {\ensuremath{\sim}} 1.77{\texttimes} speedup depending on the original vocabulary size) yet still delivering comparable or better performance than the CTC baseline. We also show that CoLaCTC successfully generalizes to CTC regularization regardless of using transcript or translation for labeling.
Exploring Category Structure with Contextual Language Models and Lexical Semantic Networks (2023)

The psychological plausibility of word embeddings has been studied through different tasks such as word similarity, semantic priming, and lexical entailment. Recent work on predicting category structure with word embeddings report low correlations with human ratings. (Heyman and Heyman, 2019) showed that static word embeddings fail at predicting typicality using cosine similarity between category and exemplar words, while (Misra et al., 2021)obtain equally modest results for various contextual language models (CLMs) using a Cloze task formulation over hand-crafted taxonomic sentences. In this work, we test a wider array of methods for probing CLMs for predicting typicality scores. Our experiments, using BERT (Devlin et al., 2018), show the importance of using the right type of CLM probes, as our best BERT-based typicality prediction methods improve on previous works. Second, our results highlight the importance of polysemy in this task, as our best results are obtained when contextualization is paired with a disambiguation mechanism as in (Chronis and Erk, 2020). Finally, additional experiments and analyses reveal that Information Content-based WordNet (Miller, 1995) similarities with disambiguation match the performance of the best BERT-based method, and in fact capture complementary information, and when combined with BERT allow for enhanced typicality predictions.
An Empirical Study of Clinical Note Generation from Doctor-Patient Encounters (2023)

Medical doctors spend on average 52 to 102 minutes per day writing clinical notes from their patient encounters (Hripcsak et al., 2011). Reducing this workload calls for relevant and efficient summarization methods. In this paper, we introduce new resources and empirical investigations for the automatic summarization of doctor-patient conversations in a clinical setting. In particular, we introduce the MTS-Dialog dataset; a new collection of 1,700 doctor-patient dialogues and corresponding clinical notes. We use this new dataset to investigate the feasibility of this task and the relevance of existing language models, data augmentation, and guided summarization techniques. We compare standard evaluation metrics based on n-gram matching, contextual embeddings, and Fact Extraction to assess the accuracy and the factual consistency of the generated summaries. To ground these results, we perform an expert-based evaluation using relevant natural language generation criteria and task-specific criteria such as critical omissions, and study the correlation between the automatic metrics and expert judgments. To the best of our knowledge, this study is the first attempt to introduce an open dataset of doctor-patient conversations and clinical notes, with detailed automated and manual evaluations of clinical note generation.
Instruction Clarification Requests in Multimodal Collaborative Dialogue Games: Tasks, and an Analysis of the CoDraw Dataset (2023)

In visual instruction-following dialogue games, players can engage in repair mechanisms in face of an ambiguous or underspecified instruction that cannot be fully mapped to actions in the world. In this work, we annotate Instruction Clarification Requests (iCRs) in CoDraw, an existing dataset of interactions in a multimodal collaborative dialogue game. We show that it contains lexically and semantically diverse iCRs being produced self-motivatedly by players deciding to clarify in order to solve the task successfully. With 8.8k iCRs found in 9.9k dialogues, CoDraw-iCR (v1) is a large spontaneous iCR corpus, making it a valuable resource for data-driven research on clarification in dialogue. We then formalise and provide baseline models for two tasks: Determining when to make an iCR and how to recognise them, in order to investigate to what extent these tasks are learnable from data.
Can Synthetic Text Help Clinical Named Entity Recognition? A Study of Electronic Health Records in French (2023)

In sensitive domains, the sharing of corpora is restricted due to confidentiality, copyrights or trade secrets. Automatic text generation can help alleviate these issues by producing synthetic texts that mimic the linguistic properties of real documents while preserving confidentiality. In this study, we assess the usability of synthetic corpus as a substitute training corpus for clinical information extraction. Our goal is to automatically produce a clinical case corpus annotated with clinical entities and to evaluate it for a named entity recognition (NER) task. We use two auto-regressive neural models partially or fully trained on generic French texts and fine-tuned on clinical cases to produce a corpus of synthetic clinical cases. We study variants of the generation process: (i) fine-tuning on annotated vs. plain text (in that case, annotations are obtained a posteriori) and (ii) selection of generated texts based on models parameters and filtering criteria. We then train NER models with the resulting synthetic text and evaluate them on a gold standard clinical corpus. Our experiments suggest that synthetic text is useful for clinical NER.