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Improving neural machine translation with neural syntactic distance

  • 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

The explicit use of syntactic information has been proved useful for neural machine translation (NMT). However, previous methods resort to either tree-structured neural networks or long linearized sequences, both of which are inefficient. Neural syntactic distance (NSD) enables us to represent a constituent tree using a sequence whose length is identical to the number of words in the sentence. NSD has been used for constituent parsing, but not in machine translation. We propose five strategies to improve NMT with NSD. Experiments show that it is not trivial to improve NMT with NSD; however, the proposed strategies are shown to improve translation performance of the baseline model (+2.1 (En-Ja), +1.3 (Ja-En), +1.2 (En-Ch), and +1.0 (Ch-En) BLEU).

Original languageEnglish
Title of host publicationLong and Short Papers
PublisherAssociation for Computational Linguistics (ACL)
Pages2032-2037
Number of pages6
ISBN (Electronic)9781950737130
StatePublished - 2019
Event2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2019 - Minneapolis, United States
Duration: 2 Jun 20197 Jun 2019

Publication series

NameNAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference
Volume1

Conference

Conference2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2019
Country/TerritoryUnited States
CityMinneapolis
Period2/06/197/06/19

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