Modified distortion matrices for phrase-based statistical machine translation

  • Authors:
  • Arianna Bisazza;Marcello Federico

  • Affiliations:
  • Fondazione Bruno Kessler, Trento, Italy;Fondazione Bruno Kessler, Trento, Italy

  • Venue:
  • ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
  • Year:
  • 2012

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Abstract

This paper presents a novel method to suggest long word reorderings to a phrase-based SMT decoder. We address language pairs where long reordering concentrates on few patterns, and use fuzzy chunk-based rules to predict likely reorderings for these phenomena. Then we use reordered n-gram LMs to rank the resulting permutations and select the n-best for translation. Finally we encode these reorderings by modifying selected entries of the distortion cost matrix, on a per-sentence basis. In this way, we expand the search space by a much finer degree than if we simply raised the distortion limit. The proposed techniques are tested on Arabic-English and German-English using well-known SMT benchmarks.