A decoder for syntax-based statistical MT

  • Authors:
  • Kenji Yamada;Kevin Knight

  • Affiliations:
  • University of Southern California, Marina del Rey, CA;University of Southern California, Marina del Rey, CA

  • Venue:
  • ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
  • Year:
  • 2002

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Abstract

This paper describes a decoding algorithm for a syntax-based translation model (Yamada and Knight, 2001). The model has been extended to incorporate phrasal translations as presented here. In contrast to a conventional word-to-word statistical model, a decoder for the syntax-based model builds up an English parse tree given a sentence in a foreign language. As the model size becomes huge in a practical setting, and the decoder considers multiple syntactic structures for each word alignment, several pruning techniques are necessary. We tested our decoder in a Chinese-to-English translation system, and obtained better results than IBM Model 4. We also discuss issues concerning the relation between this decoder and a language model.