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Forest-based neural machine translation

  • Chunpeng Ma
  • , Akihiro Tamura
  • , Masao Utiyama
  • , Tiejun Zhao
  • , Eiichiro Sumita
  • Harbin Institute of Technology
  • Japan National Institute of Information and Communications Technology
  • Ehime University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Tree-based neural machine translation (NMT) approaches, although achieved impressive performance, suffer from a major drawback: they only use the 1-best parse tree to direct the translation, which potentially introduces translation mistakes due to parsing errors. For statistical machine translation (SMT), forest-based methods have been proven to be effective for solving this problem, while for NMT this kind of approach has not been attempted. This paper proposes a forest-based NMT method that translates a linearized packed forest within a simple sequence-to-sequence framework (i.e., a forest-to-string NMT model). The BLEU score of the proposed method is higher than that of the string-to-string NMT, tree-based NMT, and forest-based SMT systems.

Original languageEnglish
Title of host publicationACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)
PublisherAssociation for Computational Linguistics (ACL)
Pages1253-1263
Number of pages11
ISBN (Electronic)9781948087322
DOIs
StatePublished - 2018
Event56th Annual Meeting of the Association for Computational Linguistics, ACL 2018 - Melbourne, Australia
Duration: 15 Jul 201820 Jul 2018

Publication series

NameACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)
Volume1

Conference

Conference56th Annual Meeting of the Association for Computational Linguistics, ACL 2018
Country/TerritoryAustralia
CityMelbourne
Period15/07/1820/07/18

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