SPMT: statistical machine translation with syntactified target language phrases

  • Authors:
  • Daniel Marcu;Wei Wang;Abdessamad Echihabi;Kevin Knight

  • Affiliations:
  • Language Weaver Inc., Marina del Rey, CA;Language Weaver Inc., Marina del Rey, CA;Language Weaver Inc., Marina del Rey, CA;Language Weaver Inc., Marina del Rey, CA

  • Venue:
  • EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
  • Year:
  • 2006

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Abstract

We introduce SPMT, a new class of statistical Translation Models that use Syntactified target language Phrases. The SPMT models outperform a state of the art phrase-based baseline model by 2.64 Bleu points on the NIST 2003 Chinese-English test corpus and 0.28 points on a human-based quality metric that ranks translations on a scale from 1 to 5.