An empirical study of building a strong baseline for constituency parsing

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Short Papers)
PublisherAssociation for Computational Linguistics (ACL)
Pages612-618
Number of pages7
ISBN (Electronic)9781948087346
DOIs
Publication statusPublished - 2018
Externally publishedYes
Event56th Annual Meeting of the Association for Computational Linguistics, ACL 2018 - Melbourne, Australia
Duration: 2018 Jul 152018 Jul 20

Publication series

NameACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)
Volume2

Conference

Conference56th Annual Meeting of the Association for Computational Linguistics, ACL 2018
Country/TerritoryAustralia
CityMelbourne
Period18/7/1518/7/20

ASJC Scopus subject areas

  • Software
  • Computational Theory and Mathematics

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