Better synchronous binarization for machine translation

  • Authors:
  • Tong Xiao;Mu Li;Dongdong Zhang;Jingbo Zhu;Ming Zhou

  • Affiliations:
  • Northeastern University, Shenyang, China;Sigma Center, Beijing, China;Sigma Center, Beijing, China;Northeastern University, Shenyang, China;Sigma Center, Beijing, China

  • Venue:
  • EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
  • Year:
  • 2009

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Abstract

Binarization of Synchronous Context Free Grammars (SCFG) is essential for achieving polynomial time complexity of decoding for SCFG parsing based machine translation systems. In this paper, we first investigate the excess edge competition issue caused by a left-heavy binary SCFG derived with the method of Zhang et al. (2006). Then we propose a new binarization method to mitigate the problem by exploring other alternative equivalent binary SCFGs. We present an algorithm that iteratively improves the resulting binary SCFG, and empirically show that our method can improve a string-to-tree statistical machine translations system based on the synchronous binarization method in Zhang et al. (2006) on the NIST machine translation evaluation tasks.