Shift-reduce word reordering for machine translation

Katsuhiko Hayashi, Katsuhito Sudoh, Hajime Tsukada, Jun Suzuki, Masaaki Nagata

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

5 Citations (Scopus)

Abstract

This paper presents a novel word reordering model that employs a shift-reduce parser for inversion transduction grammars. Our model uses rich syntax parsing features for word reordering and runs in linear time. We apply it to postordering of phrase-based machine translation (PBMT) for Japanese-to-English patent tasks. Our experimental results show that our method achieves a significant improvement of +3.1 BLEU scores against 30.15 BLEU scores of the baseline PBMT system.

Original languageEnglish
Title of host publicationEMNLP 2013 - 2013 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference
PublisherAssociation for Computational Linguistics (ACL)
Pages1382-1386
Number of pages5
ISBN (Electronic)9781937284978
Publication statusPublished - 2013 Jan 1
Externally publishedYes
Event2013 Conference on Empirical Methods in Natural Language Processing, EMNLP 2013 - Seattle, United States
Duration: 2013 Oct 182013 Oct 21

Publication series

NameEMNLP 2013 - 2013 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference

Other

Other2013 Conference on Empirical Methods in Natural Language Processing, EMNLP 2013
Country/TerritoryUnited States
CitySeattle
Period13/10/1813/10/21

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Information Systems
  • Computer Vision and Pattern Recognition

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