Consistent CCG parsing over multiple sentences for improved logical reasoning

Masashi Yoshikawa, Koji Mineshima, Hiroshi Noji, Daisuke Bekki

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

In formal logic-based approaches to Recognizing Textual Entailment (RTE), a Combinatory Categorial Grammar (CCG) parser is used to parse input premises and hypotheses to obtain their logical formulas. Here, it is important that the parser processes the sentences consistently; failing to recognize a similar syntactic structure results in inconsistent predicate argument structures among them, in which case the succeeding theorem proving is doomed to failure. In this work, we present a simple method to extend an existing CCG parser to parse a set of sentences consistently, which is achieved with an inter-sentence modeling with Markov Random Fields (MRF). When combined with existing logic-based systems, our method always shows improvement in the RTE experiments on English and Japanese languages.

本文言語English
ホスト出版物のタイトルShort Papers
出版社Association for Computational Linguistics (ACL)
ページ407-412
ページ数6
ISBN(電子版)9781948087292
出版ステータスPublished - 2018
外部発表はい
イベント2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018 - New Orleans, United States
継続期間: 2018 6 12018 6 6

出版物シリーズ

名前NAACL HLT 2018 - 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference
2

Conference

Conference2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018
国/地域United States
CityNew Orleans
Period18/6/118/6/6

ASJC Scopus subject areas

  • 言語学および言語
  • 言語および言語学
  • コンピュータ サイエンスの応用

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