Non-isomorphic forest pair translation

  • Authors:
  • Hui Zhang;Min Zhang;Haizhou Li;Eng Siong Chng

  • Affiliations:
  • Institute for Infocomm Research and Nanyang Technological University and USC Information Science Institute;Institute for Infocomm Research;Institute for Infocomm Research;Nanyang Technological University

  • Venue:
  • EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
  • Year:
  • 2010

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Abstract

This paper studies two issues, non-isomorphic structure translation and target syntactic structure usage, for statistical machine translation in the context of forest-based tree to tree sequence translation. For the first issue, we propose a novel non-isomorphic translation framework to capture more non-isomorphic structure mappings than traditional tree-based and tree-sequence-based translation methods. For the second issue, we propose a parallel space searching method to generate hypothesis using tree-to-string model and evaluate its syntactic goodness using tree-to-tree/tree sequence model. This not only reduces the search complexity by merging spurious-ambiguity translation paths and solves the data sparseness issue in training, but also serves as a syntax-based target language model for better grammatical generation. Experiment results on the benchmark data show our proposed two solutions are very effective, achieving significant performance improvement over baselines when applying to different translation models.