Fuzzy Syntactic Reordering for Phrase-based Statistical Machine Translation

Jacob Andreas, Nizar Habash, Owen Rambow

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

Abstract

The quality of Arabic-English statistical machine translation often suffers as a result of standard phrase-based SMT systems' inability to perform long-range re-orderings, specifically those needed to translate VSO-ordered Arabic sentences. This problem is further exacerbated by the low performance of Arabic parsers on subject and subject span detection. In this paper, we present two parse “fuzzification” techniques which allow the translation system to select among a range of possible S-V re-orderings. With this approach, we demonstrate a 0.3-point improvement in BLEU score (69% of the maximum possible using gold parses), and a corresponding improvement in the percentage of syntactically well-formed subjects under a manual evaluation.

Original languageEnglish (US)
Title of host publicationWMT 2011 - 6thWorkshop on Statistical Machine Translation, Proceedings of the Workshop
PublisherAssociation for Computational Linguistics (ACL)
Pages227-236
Number of pages10
ISBN (Electronic)9781937284121
StatePublished - 2011
Event6thWorkshop on Statistical Machine Translation, WMT 2011 - Edinburgh, United Kingdom
Duration: Jul 30 2011Jul 31 2011

Publication series

NameWMT 2011 - 6thWorkshop on Statistical Machine Translation, Proceedings of the Workshop

Conference

Conference6thWorkshop on Statistical Machine Translation, WMT 2011
Country/TerritoryUnited Kingdom
CityEdinburgh
Period7/30/117/31/11

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Language and Linguistics
  • Software

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