{"total":22,"rows":[{"id":"2104.08661","title":"Explaining Answers with Entailment Trees","created":"2021-04-17","cited_by_count":152,"area":null},{"id":"2102.12060","title":"Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processing","created":"2021-02-24","cited_by_count":126,"area":null},{"id":"2104.07644","title":"ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoning","created":"2021-04-15","cited_by_count":58,"area":null},{"id":"2104.06598","title":"AR-LSAT: Investigating Analytical Reasoning of Text","created":"2021-04-14","cited_by_count":51,"area":null},{"id":"2108.00648","title":"From LSAT: The Progress and Challenges of Complex Reasoning","created":"2021-08-02","cited_by_count":43,"area":null},{"id":"2110.13472","title":"Decomposing Complex Questions Makes Multi-Hop QA Easier and More Interpretable","created":"2021-10-26","cited_by_count":35,"area":null},{"id":"2106.11988","title":"On the Diversity and Limits of Human Explanations","created":"2021-06-22","cited_by_count":30,"area":null},{"id":"2407.08188","title":"Position: Measure Dataset Diversity, Don't Just Claim It","created":"2024-07-11","cited_by_count":26,"area":null},{"id":"2210.12152","title":"WikiWhy: Answering and Explaining Cause-and-Effect Questions","created":"2022-10-21","cited_by_count":21,"area":null},{"id":"2109.10604","title":"NOAHQA: Numerical Reasoning with Interpretable Graph Question Answering Dataset","created":"2021-09-22","cited_by_count":14,"area":null},{"id":"2109.06853","title":"Summarize-then-Answer: Generating Concise Explanations for Multi-hop Reading Comprehension","created":"2021-09-14","cited_by_count":10,"area":null},{"id":"2407.03545","title":"On Evaluating Explanation Utility for Human-AI Decision Making in NLP","created":"2024-07-03","cited_by_count":9,"area":null},{"id":"2403.20322","title":"Towards a Framework for Evaluating Explanations in Automated Fact Verification","created":"2024-03-29","cited_by_count":7,"area":null},{"id":"2109.03334","title":"On the Challenges of Evaluating Compositional Explanations in Multi-Hop Inference: Relevance, Completeness, and Expert Ratings","created":"2021-09-07","cited_by_count":5,"area":null},{"id":"2209.01824","title":"A Survey on Measuring and Mitigating Reasoning Shortcuts in Machine Reading Comprehension","created":"2022-09-05","cited_by_count":4,"area":null},{"id":"2403.06410","title":"A Logical Pattern Memory Pre-trained Model for Entailment Tree Generation","created":"2024-03-11","cited_by_count":3,"area":null},{"id":"2403.09703","title":"Concept-aware Data Construction Improves In-context Learning of Language Models","created":"2024-03-08","cited_by_count":2,"area":null},{"id":"2212.04070","title":"A Comprehensive Survey on Multi-hop Machine Reading Comprehension Datasets and Metrics","created":"2022-12-08","cited_by_count":1,"area":null},{"id":"2305.15130","title":"On Degrees of Freedom in Defining and Testing Natural Language Understanding","created":"2023-05-24","cited_by_count":1,"area":null},{"id":"2503.04232","title":"Tgea: An error-annotated dataset and benchmark tasks for text generation from pretrained language models","created":"2025-03-06","cited_by_count":1,"area":null},{"id":"2310.12467","title":"Contrastive Learning for Inference in Dialogue","created":"2023-10-19","cited_by_count":0,"area":null},{"id":"2408.03633","title":"CARE: A Clue-guided Assistant for CSRs to Read User Manuals","created":"2024-08-07","cited_by_count":0,"area":null}],"next_cursor":null}