Using a dependency parser to improve SMT for subject-object-verb languages

  • Authors:
  • Peng Xu;Jaeho Kang;Michael Ringgaard;Franz Och

  • Affiliations:
  • Google Inc., Mountain View, CA;Google Inc., Mountain View, CA;Google Inc., Mountain View, CA;Google Inc., Mountain View, CA

  • Venue:
  • NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

We introduce a novel precedence reordering approach based on a dependency parser to statistical machine translation systems. Similar to other preprocessing reordering approaches, our method can efficiently incorporate linguistic knowledge into SMT systems without increasing the complexity of decoding. For a set of five subject-object-verb (SOV) order languages, we show significant improvements in BLEU scores when translating from English, compared to other reordering approaches, in state-of-the-art phrase-based SMT systems.