Maximum entropy based phrase reordering model for statistical machine translation

  • Authors:

  • Deyi Xiong;Qun Liu;Shouxun Lin

  • Affiliations:

  • Institute of Computing Technology, Beijing, China;Institute of Computing Technology, Beijing, China;Institute of Computing Technology, Beijing, China

  • Venue:
  • ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
  • Year:
  • 2006

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Abstract

We propose a novel reordering model for phrase-based statistical machine translation (SMT) that uses a maximum entropy (MaxEnt) model to predicate reorderings of neighbor blocks (phrase pairs). The model provides content-dependent, hierarchical phrasal reordering with generalization based on features automatically learned from a real-world bitext. We present an algorithm to extract all reordering events of neighbor blocks from bilingual data. In our experiments on Chinese-to-English translation, this MaxEnt-based reordering model obtains significant improvements in BLEU score on the NIST MT-05 and IWSLT-04 tasks.