Discriminative reordering models for statistical machine translation

  • Authors:
  • Richard Zens;Hermann Ney

  • Affiliations:
  • RWTH Aachen University, Aachen, Germany;RWTH Aachen University, Aachen, Germany

  • Venue:
  • StatMT '06 Proceedings of the Workshop on Statistical Machine Translation
  • Year:
  • 2006

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Abstract

We present discriminative reordering models for phrase-based statistical machine translation. The models are trained using the maximum entropy principle. We use several types of features: based on words, based on word classes, based on the local context. We evaluate the overall performance of the reordering models as well as the contribution of the individual feature types on a word-aligned corpus. Additionally, we show improved translation performance using these reordering models compared to a state-of-the-art baseline system.