{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,5]],"date-time":"2025-10-05T19:49:03Z","timestamp":1759693743151,"version":"3.41.0"},"reference-count":52,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2020,9,16]],"date-time":"2020-09-16T00:00:00Z","timestamp":1600214400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"NSF","award":["IIS-1813662"],"award-info":[{"award-number":["IIS-1813662"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2020,10,31]]},"abstract":"<jats:p>Natural language inference (NLI) is the task of detecting the existence of entailment or contradiction in a given sentence pair. Although NLI techniques could help numerous information retrieval tasks, most solutions for NLI are neural approaches whose lack of interpretability prohibits both straightforward integration and diagnosis for further improvement. We target the task of generating token-level explanations for NLI from a neural model. Many existing approaches for token-level explanation are either computationally costly or require additional annotations for training. In this article, we first introduce a novel method for training an explanation generator that does not require additional human labels. Instead, the explanation generator is trained with the objective of predicting how the model\u2019s classification output will change when parts of the inputs are modified. Second, we propose to build an explanation generator in a multi-task learning setting along with the original NLI task so the explanation generator can utilize the model\u2019s internal behavior. The experiment results suggest that the proposed explanation generator outperforms numerous strong baselines. In addition, our method does not require excessive additional computation at prediction time, which renders it an order of magnitude faster than the best-performing baseline.<\/jats:p>","DOI":"10.1145\/3418052","type":"journal-article","created":{"date-parts":[[2020,9,16]],"date-time":"2020-09-16T10:12:32Z","timestamp":1600251152000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Explaining Text Matching on Neural Natural Language Inference"],"prefix":"10.1145","volume":"38","author":[{"given":"Youngwoo","family":"Kim","sequence":"first","affiliation":[{"name":"University of Massachusetts Amherst, Amherst, MA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Myungha","family":"Jang","sequence":"additional","affiliation":[{"name":"University of Massachusetts Amherst, Amherst, MA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"James","family":"Allan","sequence":"additional","affiliation":[{"name":"University of Massachusetts Amherst, Amherst, MA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,9,16]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13326-016-0083-z"},{"volume-title":"Proceedings of the 6th International Conference on Learning Representations (ICLR\u201918)","year":"2018","author":"Ancona Marco","key":"e_1_2_1_2_1"},{"volume-title":"Tickle","year":"1995","author":"Andrews Robert","key":"e_1_2_1_3_1"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0181142"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W17-5221"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0130140"},{"volume-title":"Proceedings of the Conference on Empirical Methods in Natural Language Processing. 632\u2013642","author":"Bowman Samuel","key":"e_1_2_1_7_1"},{"volume-title":"Proceedings of the 32nd Annual Conference on Neural Information Processing Systems (NIPS\u201918)","year":"2018","author":"Camburu Oana-Maria","key":"e_1_2_1_8_1"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1386"},{"volume-title":"Proceedings of the ACM SIGIR International Conference on Theory of Information Retrieval. 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