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Intell. Syst. Technol."],"published-print":{"date-parts":[[2020,6,30]]},"abstract":"<jats:p>\n            In this article, we address the multiple-choice machine comprehension (MC) problem in natural language processing. Existing approaches for MC are usually designed for general cases; however, we specially develop a novel method for solving the multiple-choice MC problem. We take the inspiration generative adversarial networks (GANs) and first propose an adversarial framework for multiple-choice oriented MC, named\n            <jats:italic>McGAN<\/jats:italic>\n            . Specifically, our approach is designed as a GAN-based method that unifies both generative and discriminative MC models. Working together, the generative model focuses on predicting relevant answer given a passage (text) and a question; the discriminative model focuses on predicting their relevancy given an answer-passage-question set. Based on the competition via adversarial training in a minimize-maximize game, the proposed method takes advantages from both models. To evaluate the performance, we test our McGAN model on three well-known datasets for multiple-choice MC. Our results show that McGAN can achieve a significant increase in accuracy compared to existing models based on all three datasets, and it consistently outperforms all tested baselines, including state-of-the-art techniques.\n          <\/jats:p>","DOI":"10.1145\/3372120","type":"journal-article","created":{"date-parts":[[2020,4,3]],"date-time":"2020-04-03T14:20:03Z","timestamp":1585923603000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Unified Generative Adversarial Networks for Multiple-Choice Oriented Machine Comprehension"],"prefix":"10.1145","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4695-6345","authenticated-orcid":false,"given":"Zhuang","family":"Liu","sequence":"first","affiliation":[{"name":"Dalian University of Technology, Dalian, Liaoning Province, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6494-1174","authenticated-orcid":false,"given":"Keli","family":"Xiao","sequence":"additional","affiliation":[{"name":"Stony Brook University, Stony Brook, NY"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4094-7499","authenticated-orcid":false,"given":"Bo","family":"Jin","sequence":"additional","affiliation":[{"name":"Dalian University of Technology, Dalian, Liaoning Province, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaiyu","family":"Huang","sequence":"additional","affiliation":[{"name":"Dalian University of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Degen","family":"Huang","sequence":"additional","affiliation":[{"name":"Dalian University of Technology, Dalian, Liaoning Province, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunxia","family":"Zhang","sequence":"additional","affiliation":[{"name":"Dalian University of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,4,3]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI\u201916)","author":"Abadi Mart\u00edn","year":"2016","unstructured":"Mart\u00edn Abadi , Paul Barham , Jianmin Chen , Zhifeng Chen , Andy Davis , Jeffrey Dean , Matthieu Devin , 2016 . 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In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI-18) the 30th Conference on Innovative Applications of Artificial Intelligence (IAAI-18) and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18). 6077--6085. https:\/\/www.aaai.org\/ocs\/index.php\/AAAI\/AAAI18\/paper\/view\/16331."}],"container-title":["ACM Transactions on Intelligent Systems and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3372120","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3372120","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T23:45:06Z","timestamp":1750203906000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3372120"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,3]]},"references-count":44,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2020,6,30]]}},"alternative-id":["10.1145\/3372120"],"URL":"https:\/\/doi.org\/10.1145\/3372120","relation":{},"ISSN":["2157-6904","2157-6912"],"issn-type":[{"type":"print","value":"2157-6904"},{"type":"electronic","value":"2157-6912"}],"subject":[],"published":{"date-parts":[[2020,4,3]]},"assertion":[{"value":"2019-04-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-11-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-04-03","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}