Improved statistical alignment models

  • Authors:
  • Franz Josef Och;Hermann Ney

  • Affiliations:
  • University of Technology, Aachen, Germany;University of Technology, Aachen, Germany

  • Venue:
  • ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
  • Year:
  • 2000

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Abstract

In this paper, we present and compare various single-word based alignment models for statistical machine translation. We discuss the five IBM alignment models, the Hidden-Markov alignment model, smoothing techniques and various modifications. We present different methods to combine alignments. As evaluation criterion we use the quality of the resulting Viterbi alignment compared to a manually produced reference alignment. We show that models with a first-order dependence and a fertility model lead to significantly better results than the simple models IBM-1 or IBM-2, which are not able to go beyond zero-order dependencies.