Improving reordering for statistical machine translation with smoothed priors and syntactic features

  • Authors:
  • Bing Xiang;Niyu Ge;Abraham Ittycheriah

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

  • Venue:
  • SSST-5 Proceedings of the Fifth Workshop on Syntax, Semantics and Structure in Statistical Translation
  • Year:
  • 2011

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Abstract

In this paper we propose several novel approaches to improve phrase reordering for statistical machine translation in the framework of maximum-entropy-based modeling. A smoothed prior probability is introduced to take into account the distortion effect in the priors. In addition to that we propose multiple novel distortion features based on syntactic parsing. A new metric is also introduced to measure the effect of distortion in the translation hypotheses. We show that both smoothed priors and syntax-based features help to significantly improve the reordering and hence the translation performance on a large-scale Chinese-to-English machine translation task.