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Lexicalized Syntactic Reordering Framework for Word Alignment and Machine Translation

  • Chung-chi Huang
  • Wei-teh Chen
  • Jason S. Chang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5459)

Abstract

We propose a lexicalized syntactic reordering framework for cross-language word aligning and translating researches. In this framework, we first flatten hierarchical source-language parse trees into syntactically-motivated linear string representations, which can easily be input to many feature-like probabilistic models. During model training, these string representations accompanied with target-language word alignment information are leveraged to learn systematic similarities and differences in languages’ grammars. At runtime, syntactic constituents of source-language parse trees will be reordered according to automatically acquired lexicalized reordering rules in previous step, to closer match word orientations of the target language. Empirical results show that, as a preprocessing component, bilingual word aligning and translating tasks benefit from our reordering methodology.

Keywords

word alignment machine translation phrase-based decoder and syntactic reordering rule 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Chung-chi Huang
    • 1
  • Wei-teh Chen
    • 1
  • Jason S. Chang
    • 1
  1. 1.Institute of Information Systems and ApplicationNational Tsing Hua UniversityHsinchuTaiwan

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