Mixture-model adaptation for SMT

  • Authors:
  • George Foster;Roland Kuhn

  • Affiliations:
  • National Research Council, Canada;National Research Council, Canada

  • Venue:
  • StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
  • Year:
  • 2007

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Abstract

We describe a mixture-model approach to adapting a Statistical Machine Translation System for new domains, using weights that depend on text distances to mixture components. We investigate a number of variants on this approach, including cross-domain versus dynamic adaptation; linear versus loglinear mixtures; language and translation model adaptation; different methods of assigning weights; and granularity of the source unit being adapted to. The best methods achieve gains of approximately one BLEU percentage point over a state-of-the art non-adapted baseline system.