{"type":"ScholarlyArticle","title":"Answer Generation through Unified Memories over Multiple Passages","authors":[{"name":"Makoto Nakatsuji","slug":"makoto-nakatsuji","given":"Makoto","family":"Nakatsuji","affiliation":"NTT Resonant Inc. Granparktower , 3-4-1 Shibaura , Minato-ku , Tokyo 108-0023 , Japan"},{"name":"Sohei Okui","slug":"sohei-okui","given":"Sohei","family":"Okui","affiliation":"NTT Resonant Inc. Granparktower , 3-4-1 Shibaura , Minato-ku , Tokyo 108-0023 , Japan"}],"abstract":"Machine reading comprehension methods that gen- erate answers by referring to multiple passages for a question have gained much attention in AI and NLP communities. The current methods, however, do not investigate the relationships among multi- ple passages in the answer generation process, even though topics correlated among the passages may be answer candidates. Our method, called neural answer Generation through Unified Memories over Multiple Passages (GUM-MP), solves this problem as follows. First, it determines which tokens in the passages are matched to the question. In particular, it investigates matches between tokens in positive passages, which are assigned to the question, and those in negative passages, which are not related to the question. Next, it determines which tokens in the passage are matched to other passages assigned to the same question and at the same time it investi- gates the topics in which they are matched. Finally, it encodes the token sequences with the above two matching results into unified memories in the pas- sage encoders and learns the answer sequence by using an encoder-decoder with a multiple-pointer- generator mechanism. As a result, GUM-MP can generate answers by pointing to important tokens present across passages. Evaluations indicate that GUM-MP generates much more accurate results than the current models do.","doi":"10.24963/ijcai.2020/529","url":"https://ijcai.org/proceedings/2020/529","pdf":"https://ijcai.org/proceedings/2020/0529.pdf","markdown":"https://ijcai.org/proceedings/2020/529.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/529","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Natural Language Processing","track":"Main track","number":529,"pages":{"first":3823,"last":3829},"keywords":["Natural Language Processing: Question Answering","Natural Language Processing: Natural Language Generation","Natural Language Processing: Natural Language Processing"],"videos":[],"references":[{"position":1,"raw":"[Devlin et al., 2018] Jacob Devlin, Ming-Wei Chang, Ken- ton Lee, and Kristina Toutanova. 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