Soft syntactic constraints for word alignment through discriminative training

  • Authors:
  • Colin Cherry;Dekang Lin

  • Affiliations:
  • University of Alberta, Canada;Google Inc., Mountain View, CA

  • Venue:
  • COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
  • Year:
  • 2006

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Abstract

Word alignment methods can gain valuable guidance by ensuring that their alignments maintain cohesion with respect to the phrases specified by a monolingual dependency tree. However, this hard constraint can also rule out correct alignments, and its utility decreases as alignment models become more complex. We use a publicly available structured output SVM to create a max-margin syntactic aligner with a soft cohesion constraint. The resulting aligner is the first, to our knowledge, to use a discriminative learning method to train an ITG bitext parser.