Lexicalized Syntactic Reordering Framework for Word Alignment and Machine Translation

  • Chung-chi Huang
  • Wei-teh Chen
  • Jason S. Chang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5459)


We propose a lexicalized syntactic reordering framework for cross-language word aligning and translating researches. In this framework, we first flatten hierarchical source-language parse trees into syntactically-motivated linear string representations, which can easily be input to many feature-like probabilistic models. During model training, these string representations accompanied with target-language word alignment information are leveraged to learn systematic similarities and differences in languages’ grammars. At runtime, syntactic constituents of source-language parse trees will be reordered according to automatically acquired lexicalized reordering rules in previous step, to closer match word orientations of the target language. Empirical results show that, as a preprocessing component, bilingual word aligning and translating tasks benefit from our reordering methodology.


word alignment machine translation phrase-based decoder and syntactic reordering rule 


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  1. 1.
    Wu, D.: Stochastic inversion transduction grammars and bilingual parsing of parallel corpora. Computational Linguistics 23(3), 377–403 (1997)Google Scholar
  2. 2.
    Zens, R., Ney, H.: A comparative study on reordering constraints in statistical machine translation. In: ACL, pp. 144–151 (2003)Google Scholar
  3. 3.
    Yamada, K., Knight, K.: A syntax-based statistical translation model. In: ACL, pp. 523–530 (2001)Google Scholar
  4. 4.
    Chiang, D.: A hierarchical phrase-based model for statistical machine translation. In: ACL, pp. 263–270 (2005)Google Scholar
  5. 5.
    Galley, M., Graehl, J., Knight, K., Marcu, D., DeNeefe, S., Wang, W., Thayer, I.: Scalable Inference and Training of Context-Rich Syntactic Translation Models. In: ACL, pp. 961–968 (2006)Google Scholar
  6. 6.
    Zhang, D., Li, M., Li, C.-h., Zhou, M.: Phrase reordering model integrating syntactic knowledge for SMT. In: EMNLP/CoNLL, pp. 533–540 (2007)Google Scholar
  7. 7.
    Liu, Y., Huang, Y., Liu, Q., Lin, S.: Forest-to-string statistical translation rules. In: ACL, pp. 704–711 (2007)Google Scholar
  8. 8.
    Wang, C., Collins, M., Koehn, P.: Chinese syntactic reordering for statistical machine translation. In: EMNLP/CoNLL, pp. 737–745 (2007)Google Scholar
  9. 9.
    Xiong, D., Liu, Q., Lin, S.: Maximum entropy based phrase reordering model for statistical machine translation. In: ACL/COLING, pp. 521–528 (2006)Google Scholar
  10. 10.
    Zhang, H., Gildea, D.: Stochastic lexicalized inversion transduction grammar for alignment. In: ACL, pp. 475–482 (2005)Google Scholar
  11. 11.
    Zhang, H., Huang, L., Gildea, D., Knight, K.: Synchronous binarization for machine translation. In: NAACL/HLT, pp. 256–263 (2006)Google Scholar
  12. 12.
    Koehn, P., Och, F., Marcu, D.: Statistical phrase-based translation. In: NAACL/HLT, pp. 48–54 (2003)Google Scholar
  13. 13.
    Papineni, K., Roukos, S., Ward, T., Zhu, W.-j.: BLEU: a method for automatic evaluation of machine translation. In: ACL, pp. 311–318 (2002)Google Scholar
  14. 14.
    Ayan, N.F., Dorr, B.J.: Going beyond AER: an extensive analysis of word alignments and their impact on MT. In: ACL, pp. 9–16 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Chung-chi Huang
    • 1
  • Wei-teh Chen
    • 1
  • Jason S. Chang
    • 1
  1. 1.Institute of Information Systems and ApplicationNational Tsing Hua UniversityHsinchuTaiwan

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