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Abstract

This paper proposed a flattened syntactical phrase-based translation model for Statistical Machine Translation (SMT) learned from bilingual parallel parsed texts. The flattened syntactical phrases are sets of ordered leaf nodes with their father nodes of single syntax trees or forests ignoring the inner structure, containing lexicalized terminals and non-terminals as variable nodes. Constraints over the variable nodes in target side guarantee correct syntactical structures of translations in accordant to the syntactical knowledge learned from parallel texts. The experiments based on Chinese-to-English translation show us a predictable result that our model achieves 1.87% and 4.76% relative improvements, over Pharaoh, the state-of-art phrase-based translation system, and the system of traditional tree-to-tree model based on STSG.

Keywords

flattened syntactical phrase synchronous grammar syntactical structure 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Qing Chen
    • 1
  • Tianshun Yao
    • 1
  1. 1.Natural Language Processing LabNortheastern UniversityShenyangP.R. China

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