Dependency-based bracketing transduction grammar for statistical machine translation

  • Authors:
  • Jinsong Su;Yang Liu;Haitao Mi;Hongmei Zhao;Yajuan Lü;Qun Liu

  • Affiliations:
  • Chinese Academy of Sciences;Chinese Academy of Sciences;Chinese Academy of Sciences;Chinese Academy of Sciences;Chinese Academy of Sciences;Chinese Academy of Sciences

  • Venue:
  • COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
  • Year:
  • 2010

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Abstract

In this paper, we propose a novel dependency-based bracketing transduction grammar for statistical machine translation, which converts a source sentence into a target dependency tree. Different from conventional bracketing transduction grammar models, we encode target dependency information into our lexical rules directly, and then we employ two different maximum entropy models to determine the reordering and combination of partial dependency structures, when we merge two neighboring blocks. By incorporating dependency language model further, large-scale experiments on Chinese-English task show that our system achieves significant improvements over the baseline system on various test sets even with fewer phrases.