| Challenge: | Word associations are among the most common paradigms to study the human mental lexicon. |
| Approach: | They present a large dataset of word associations with explanations and relation labels . they show that current language models struggle to capture the diversity of human associations . |
| Outcome: | The proposed model fails to capture the diversity of human associations, the authors show . they show that the model is a rich benchmark for commonsense modeling and generation. |
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WordTies: Measuring Word Associations in Language Models via Constrained Sampling (2022.findings-emnlp)
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| Challenge: | Word associations are widely used in psychology to provide insights on how humans perceive and understand concepts. |
| Approach: | They propose an algorithm that allows an asymmetric measurement of associated words, given a cue word as input. |
| Outcome: | The proposed algorithm shares more overlap with human associations and observes the asymmetric property of human associations. |
SHAP-Based Explanation Methods: A Review for NLP Interpretability (2022.coling-1)
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| Challenge: | Existing models with opacity problems have been proposed to address this problem. |
| Approach: | They propose a unified local-interpretability framework with a rigorous theoretical foundation on the game-theoretic concept of Shapley values. |
| Outcome: | The proposed framework is based on the Shapley-value-based model explanations. |
A guide to the dataset explosion in QA, NLI, and commonsense reasoning (2020.coling-tutorials)
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| Challenge: | a tutorial aims to provide an up-to-date guide to the recent datasets . the target audience is the NLP practitioners who are lost in dozens of the recent data sets. |
| Approach: | This tutorial provides an up-to-date guide to the recent datasets . it surveys old and new methodological issues with dataset construction . |
| Outcome: | This tutorial aims to provide an up-to-date guide to the recent datasets . it surveys the old and new methodological issues with dataset construction . |
Explanation in the Era of Large Language Models (2024.naacl-tutorials)
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| Challenge: | Explanation has long been a part of communication, where humans use language to elucidate each other and transmit information about mechanisms of events. |
| Approach: | They review the opportunities and challenges of explanations in the era of large language models and examine how they can be used to generate explanations. |
| Outcome: | The proposed methods are based on the models of large language models (LLMs) and their opaque nature. |
Commonsense Reasoning for Natural Language Processing (2020.acl-tutorials)
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| Challenge: | In this tutorial, we will outline the various types of commonsense knowledge and discuss techniques to gather and represent commonsence knowledge. |
| Approach: | This tutorial will provide researchers with the critical foundations and recent advances in commonsense representation and reasoning. |
| Outcome: | This tutorial will outline the various types of commonsense and discuss techniques to gather and represent commonsence knowledge while highlighting the challenges specific to this type of knowledge (e.g., reporting bias). |
Learning to Explain: Datasets and Models for Identifying Valid Reasoning Chains in Multihop Question-Answering (2020.emnlp-main)
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| Challenge: | despite rapid progress in multihop question-answering, models still have trouble explaining why an answer is correct. |
| Approach: | They propose three explanation datasets in which explanations from corpus facts are annotated . they first annotate multiple candidate explanations for each answer, then use crowd-sourcing perturbations to test generalization . |
| Outcome: | The proposed datasets improve explanation quality but still behind the upper bound . the proposed dataset can be used to improve explanations using a BERT-based classifier . |
English Machine Reading Comprehension Datasets: A Survey (2021.emnlp-main)
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| Challenge: | a survey of English Machine Reading Comprehension datasets is carried out . the aim is to provide a concise yet informative overview of the landscape . |
| Approach: | They survey 60 English Machine Reading Comprehension datasets to provide a resource for other researchers interested in this problem. |
| Outcome: | The proposed survey covers 60 English MRC datasets with a view to providing a resource for other researchers interested in the problem. |
Commonsense about Human Senses: Labeled Data Collection Processes (D19-60)
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| Challenge: | Existing methods for recognizing mentions of human senses in text are lacking in common sense knowledge acquisition. |
| Approach: | They propose to use machine learning to acquire labeled data to extract common sense relationships pertaining to sense perception concepts. |
| Outcome: | The proposed method is effective when used with standard machine learning models on the task of sense recognition in text. |
Interpretable Text Embeddings and Text Similarity Explanation: A Survey (2025.emnlp-main)
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| Challenge: | Text embeddings are a fundamental component in many NLP tasks, but their interpretation and explanation remain challenging. |
| Approach: | They propose a framework for interpretable text embeddings and text similarity explanation . they characterize the main ideas, approaches, and trade-offs and discuss lessons learned . |
| Outcome: | The proposed methods are compared with existing models and compare them with existing ones. |
A Survey of the State of Explainable AI for Natural Language Processing (2020.aacl-main)
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| Challenge: | Recent years have seen significant advances in the quality of state-of-the-art models, but they have come at the expense of models becoming less interpretable. |
| Approach: | This survey examines the current state of Explainable AI within the domain of NLP . they detail the operations and explainability techniques currently available for generating explanations for NLP models . |
| Outcome: | This survey examines the state of explainable AI (XAI) within the domain of natural language processing . it focuses on the operations and explainability techniques currently available for NLP models . |