Prior derivation models for formally syntax-based translation using linguistically syntactic parsing and tree kernels

  • Authors:
  • Bowen Zhou;Xiaodan Zhu;Bing Xiang;Yuqing Gao

  • Affiliations:
  • IBM T. J. Watson Research Center, Yorktown Heights, NY;University of Toronto;IBM T. J. Watson Research Center, Yorktown Heights, NY;IBM T. J. Watson Research Center, Yorktown Heights, NY

  • Venue:
  • SSST '08 Proceedings of the Second Workshop on Syntax and Structure in Statistical Translation
  • Year:
  • 2008

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Abstract

This paper presents an improved formally syntax-based SMT model, which is enriched by linguistically syntactic knowledge obtained from statistical constituent parsers. We propose a linguistically-motivated prior derivation model to score hypothesis derivations on top of the baseline model during the translation decoding. Moreover, we devise a fast training algorithm to achieve such improved models based on tree kernel methods. Experiments on an English-to-Chinese task demonstrate that our proposed models outperformed the baseline formally syntax-based models, while both of them achieved significant improvements over a state-of-the-art phrase-based SMT system.