A syntax-based statistical translation model

  • Authors:
  • Kenji Yamada;Kevin Knight

  • Affiliations:
  • University of Southern California, Marina del Rey, CA;University of Southern California, Marina del Rey, CA

  • Venue:
  • ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
  • Year:
  • 2001

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Abstract

We present a syntax-based statistical translation model. Our model transforms a source-language parse tree into a target-language string by applying stochastic operations at each node. These operations capture linguistic differences such as word order and case marking. Model parameters are estimated in polynomial time using an EM algorithm. The model produces word alignments that are better than those produced by IBM Model 5.