A probability model to improve word alignment

  • Authors:
  • Colin Cherry;Dekang Lin

  • Affiliations:
  • University of Alberta Edmonton, Alberta, Canada;University of Alberta Edmonton, Alberta, Canada

  • Venue:
  • ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
  • Year:
  • 2003

Quantified Score

Hi-index 0.00

Visualization

Abstract

Word alignment plays a crucial role in statistical machine translation. Word-aligned corpora have been found to be an excellent source of translation-related knowledge. We present a statistical model for computing the probability of an alignment given a sentence pair. This model allows easy integration of context-specific features. Our experiments show that this model can be an effective tool for improving an existing word alignment.