An empirical study of building a strong baseline for constituency parsing

Jun Suzuki, Sho Takase, Hidetaka Kamigaito, Makoto Morishita, Masaaki Nagata

研究成果: 書籍の章/レポート/Proceedings会議への寄与査読

15 被引用数 (Scopus)

抄録

This paper investigates the construction of a strong baseline based on general purpose sequence-to-sequence models for constituency parsing. We incorporate several techniques that were mainly developed in natural language generation tasks, e.g., machine translation and summarization, and demonstrate that the sequence-to-sequence model achieves the current top-notch parsers’ performance without requiring explicit task-specific knowledge or architecture of constituent parsing.

本文言語英語
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 (Short Papers)
出版社Association for Computational Linguistics (ACL)
ページ612-618
ページ数7
ISBN(電子版)9781948087346
DOI
出版ステータス出版済み - 2018
イベント56th Annual Meeting of the Association for Computational Linguistics, ACL 2018 - Melbourne, オーストラリア
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)
2

会議

会議56th Annual Meeting of the Association for Computational Linguistics, ACL 2018
国/地域オーストラリア
CityMelbourne
Period18/7/1518/7/20

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