Phrase-based statistical machine translation: a level of detail approach

  • Authors:
  • Hendra Setiawan;Haizhou Li;Min Zhang;Beng Chin Ooi

  • Affiliations:
  • Institute for Infocomm Research, Singapore;Institute for Infocomm Research, Singapore;Institute for Infocomm Research, Singapore;School of Computing, National University of Singapore, Singapore

  • Venue:
  • IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
  • Year:
  • 2005

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Abstract

The merit of phrase-based statistical machine translation is often reduced by the complexity to construct it. In this paper, we address some issues in phrase-based statistical machine translation, namely: the size of the phrase translation table, the use of underlying translation model probability and the length of the phrase unit. We present Level-Of-Detail (LOD) approach, an agglomerative approach for learning phrase-level alignment. Our experiments show that LOD approach significantly improves the performance of the word-based approach. LOD demonstrates a clear advantage that the phrase translation table grows only sub-linearly over the maximum phrase length, while having a performance comparable to those of other phrase-based approaches.