Modeling with structures in statistical machine translation

  • Authors:
  • Ye-Yi Wang;Alex Waibel

  • Affiliations:
  • Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA

  • Venue:
  • COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
  • Year:
  • 1998

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Abstract

Most statistical machine translation systems employ a word-based alignment model. In this paper we demonstrate that word-based alignment is a major cause of translation errors. We propose a new alignment model based on shallow phrase structures, and the structures can be automatically acquired from parallel corpus. This new model achieved over 10% error reduction for our spoken language translation task.