The impact of parse quality on syntactically-informed statistical machine translation

  • Authors:
  • Chris Quirk;Simon Corston-Oliver

  • Affiliations:
  • Microsoft Research, One Microsoft Way, Redmond, WA;Microsoft Research, One Microsoft Way, Redmond, WA

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

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Abstract

We investigate the impact of parse quality on a syntactically-informed statistical machine translation system applied to technical text. We vary parse quality by varying the amount of data used to train the parser. As the amount of data increases, parse quality improves, leading to improvements in machine translation output and results that significantly outperform a state-of-the-art phrasal baseline.