Multiple reorderings in phrase-based machine translation

  • Authors:
  • Niyu Ge;Abe Ittycheriah;Kishore Papineni

  • Affiliations:
  • IBM T.J.Watson Research, Yorktown Heights, NY;IBM T.J.Watson Research, Yorktown Heights, NY;Yahoo! Research, New York, NY

  • Venue:
  • SSST '08 Proceedings of the Second Workshop on Syntax and Structure in Statistical Translation
  • Year:
  • 2008

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Abstract

This paper presents a method to integrate multiple reordering strategies in phrase-based statistical machine translation. Recently there has been much research effort in reordering problems in machine translation. State-of-the-art decoders incorporate sophisticated local reordering strategies, but there is little research on a unified approach to incorporate various kinds of reordering methods. We present a phrase-based decoder which easily allows multiple reordering schemes. We show how to use this framework to perform distance-based reordering and HIERO-style (Chiang 2005) hierarchical reordering. We also present two novel syntax-based reordering methods, one built on part-of-speech tags and the other based on parse trees. We will give experimental results using these relatively easy to implement methods on standard tests.