Neural Adversarial Training for Semi-supervised Japanese Predicate-argument Structure Analysis

Shuhei Kurita, Daisuke Kawahara, Sadao Kurohashi

研究成果: Conference contribution

5 被引用数 (Scopus)

抄録

Japanese predicate-argument structure (PAS) analysis involves zero anaphora resolution, which is notoriously difficult. To improve the performance of Japanese PAS analysis, it is straightforward to increase the size of corpora annotated with PAS. However, since it is prohibitively expensive, it is promising to take advantage of a large amount of raw corpora. In this paper, we propose a novel Japanese PAS analysis model based on semi-supervised adversarial training with a raw corpus. In our experiments, our model outperforms existing state-of-the-art models for Japanese PAS analysis.

本文言語English
ホスト出版物のタイトルACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)
出版社Association for Computational Linguistics (ACL)
ページ474-484
ページ数11
ISBN(電子版)9781948087322
DOI
出版ステータスPublished - 2018
外部発表はい
イベント56th Annual Meeting of the Association for Computational Linguistics, ACL 2018 - Melbourne, Australia
継続期間: 2018 7月 152018 7月 20

出版物シリーズ

名前ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)
1

Conference

Conference56th Annual Meeting of the Association for Computational Linguistics, ACL 2018
国/地域Australia
CityMelbourne
Period18/7/1518/7/20

ASJC Scopus subject areas

  • ソフトウェア
  • 計算理論と計算数学

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