A discriminative framework for bilingual word alignment

  • Authors:
  • Robert C. Moore

  • Affiliations:
  • Microsoft Research, Redmond, WA

  • Venue:
  • HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
  • Year:
  • 2005

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Abstract

Bilingual word alignment forms the foundation of most approaches to statistical machine translation. Current word alignment methods are predominantly based on generative models. In this paper, we demonstrate a discriminative approach to training simple word alignment models that are comparable in accuracy to the more complex generative models normally used. These models have the the advantages that they are easy to add features to and they allow fast optimization of model parameters using small amounts of annotated data.