Alignment by agreement

  • Authors:
  • Percy Liang;Ben Taskar;Dan Klein

  • Affiliations:
  • UC Berkeley Berkeley, CA;UC Berkeley Berkeley, CA;UC Berkeley Berkeley, CA

  • Venue:
  • HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
  • Year:
  • 2006

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Abstract

We present an unsupervised approach to symmetric word alignment in which two simple asymmetric models are trained jointly to maximize a combination of data likelihood and agreement between the models. Compared to the standard practice of intersecting predictions of independently-trained models, joint training provides a 32% reduction in AER. Moreover, a simple and efficient pair of HMM aligners provides a 29% reduction in AER over symmetrized IBM model 4 predictions.