Using syntax to improve word alignment precision for syntax-based machine translation

  • Authors:
  • Victoria Fossum;Kevin Knight;Steven Abney

  • Affiliations:
  • University of Michigan, Ann Arbor, MI;University of Southern California, Marina del Rey, CA;University of Michigan, Ann Arbor, MI

  • Venue:
  • StatMT '08 Proceedings of the Third Workshop on Statistical Machine Translation
  • Year:
  • 2008

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Abstract

Word alignments that violate syntactic correspondences interfere with the extraction of string-to-tree transducer rules for syntax-based machine translation. We present an algorithm for identifying and deleting incorrect word alignment links, using features of the extracted rules. We obtain gains in both alignment quality and translation quality in Chinese-English and Arabic-English translation experiments relative to a GIZA++ union baseline.