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Phrasal syntactic category sequence model for phrase-based MT

  • Japan National Institute of Information and Communications Technology
  • Harbin Institute of Technology

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

Abstract

Incorporating target syntax into phrase-based machine translation (PBMT) can generate syntactically well-formed translations. We propose a novel phrasal syntactic category sequence (PSCS) model which allows a PBMT decoder to prefer more grammatical translations. We parse all the sentences on the target side of the bilingual training corpus. In the standard phrase pair extraction procedure, we assign a syntactic category to each phrase pair and build a PSCS model from the parallel training data. Then, we log linearly incorporate the PSCS model into a standard PBMT system. Our method is very simple and yields a 0.7 BLEU point improvement when compared to the baseline PBMT system.

Original languageEnglish
Title of host publicationComputational Linguistics and Intelligent Text Processing - 13th International Conference, CICLing 2012, Proceedings
PublisherSpringer Verlag
Pages52-59
Number of pages8
EditionPART 2
ISBN (Print)9783642286001
DOIs
StatePublished - 2012
Event13th Annual Conference on Intelligent Text Processing and Computational Linguistics, CICLing 2012 - New Delhi, India
Duration: 11 Mar 201217 Mar 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume7182 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th Annual Conference on Intelligent Text Processing and Computational Linguistics, CICLing 2012
Country/TerritoryIndia
CityNew Delhi
Period11/03/1217/03/12

Keywords

  • machine translation
  • natural language processing
  • phrase-based machine translation

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