Synchronous binarization for machine translation

  • Authors:
  • Hao Zhang;Liang Huang;Daniel Gildea;Kevin Knight

  • Affiliations:
  • University of Rochester, Rochester, NY;University of Pennsylvania, Philadelphia, PA;University of Rochester, Rochester, NY;University of Southern California, Marina del Rey, 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

Systems based on synchronous grammars and tree transducers promise to improve the quality of statistical machine translation output, but are often very computationally intensive. The complexity is exponential in the size of individual grammar rules due to arbitrary re-orderings between the two languages, and rules extracted from parallel corpora can be quite large. We devise a linear-time algorithm for factoring syntactic re-orderings by binarizing synchronous rules when possible and show that the resulting rule set significantly improves the speed and accuracy of a state-of-the-art syntax-based machine translation system.