Automatic generation of high quality CCGBanks for ParSER domain adaptation

Masashi Yoshikawa, Hiroshi Noji, Koji Mineshima, Daisuke Bekki

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

Abstract

We propose a new domain adaptation method for Combinatory Categorial Grammar (CCG) parsing, based on the idea of automatic generation of CCG corpora exploiting cheaper resources of dependency trees. Our solution is conceptually simple, and not relying on a specific parser architecture, making it applicable to the current best-performing parsers. We conduct extensive parsing experiments with detailed discussion; on top of existing benchmark datasets on (1) biomedical texts and (2) question sentences, we create experimental datasets of (3) speech conversation and (4) math problems. When applied to the proposed method, an off-the-shelf CCG parser shows significant performance gains, improving from 90.7% to 96.6% on speech conversation, and from 88.5% to 96.8% on math problems.

Original languageEnglish
Title of host publicationACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
PublisherAssociation for Computational Linguistics (ACL)
Pages129-139
Number of pages11
ISBN (Electronic)9781950737482
Publication statusPublished - 2020
Externally publishedYes
Event57th Annual Meeting of the Association for Computational Linguistics, ACL 2019 - Florence, Italy
Duration: 2019 Jul 282019 Aug 2

Publication series

NameACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference

Conference

Conference57th Annual Meeting of the Association for Computational Linguistics, ACL 2019
CountryItaly
CityFlorence
Period19/7/2819/8/2

ASJC Scopus subject areas

  • Language and Linguistics
  • Computer Science(all)
  • Linguistics and Language

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