Lexicalized Syntactic Reordering Framework for Word Alignment and Machine Translation

  • Authors:

  • Chung-Chi Huang;Wei-Teh Chen;Jason S. Chang

  • Affiliations:

  • Institute of Information Systems and Application, National Tsing Hua University, Hsinchu, Taiwan 300;Institute of Information Systems and Application, National Tsing Hua University, Hsinchu, Taiwan 300;Institute of Information Systems and Application, National Tsing Hua University, Hsinchu, Taiwan 300

  • Venue:
  • ICCPOL '09 Proceedings of the 22nd International Conference on Computer Processing of Oriental Languages. Language Technology for the Knowledge-based Economy
  • Year:
  • 2009

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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.