A path-based transfer model for machine translation

  • Authors:
  • Dekang Lin

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

  • Venue:
  • COLING '04 Proceedings of the 20th international conference on Computational Linguistics
  • Year:
  • 2004

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Abstract

We propose a path-based transfer model for machine translation. The model is trained with a word-aligned parallel corpus where the source language sentences are parsed. The training algorithm extracts a set of transfer rules and their probabilities from the training corpus. A rule translates a path in the source language dependency tree into a fragment in the target dependency tree. The problem of finding the most probable translation becomes a graph-theoretic problem of finding the minimum path covering of the source language dependency tree.