Improving statistical MT by coupling reordering and decoding

  • Authors:
  • Josep Maria Crego;José B. Mariño

  • Affiliations:
  • Department of Signal Theory and Communications, TALP Research Center, Universitat Politècnica de Catalunya, Barcelona, Spain 08034;Department of Signal Theory and Communications, TALP Research Center, Universitat Politècnica de Catalunya, Barcelona, Spain 08034

  • Venue:
  • Machine Translation
  • Year:
  • 2006

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Abstract

In this paper we describe an elegant and efficient approach to coupling reordering and decoding in statistical machine translation, where the n-gram translation model is also employed as distortion model. The reordering search problem is tackled through a set of linguistically motivated rewrite rules, which are used to extend a monotonic search graph with reordering hypotheses. The extended graph is traversed in the global search when a fully informed decision can be taken. Further experiments show that the n-gram translation model can be successfully used as reordering model when estimated with reordered source words. Experiments are reported on the Europarl task (Spanish---English and English---Spanish). Results are presented regarding translation accuracy and computational efficiency, showing significant improvements in translation quality with respect to monotonic search for both translation directions at a very low computational cost.