Discriminative training and maximum entropy models for statistical machine translation

  • Authors:
  • Franz Josef Och;Hermann Ney

  • Affiliations:
  • University of Technology, Aachen, Germany;University of Technology, Aachen, Germany

  • Venue:
  • ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
  • Year:
  • 2002

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Abstract

We present a framework for statistical machine translation of natural languages based on direct maximum entropy models, which contains the widely used source-channel approach as a special case. All knowledge sources are treated as feature functions, which depend on the source language sentence, the target language sentence and possible hidden variables. This approach allows a baseline machine translation system to be extended easily by adding new feature functions. We show that a baseline statistical machine translation system is significantly improved using this approach.