A novel dependency-to-string model for statistical machine translation

  • Authors:

  • Jun Xie;Haitao Mi;Qun Liu

  • Affiliations:

  • Key Laboratory of Intelligent Information Processiong, Institute of Computing Technology, Beijing, China;Key Laboratory of Intelligent Information Processiong, Institute of Computing Technology, Beijing, China;Key Laboratory of Intelligent Information Processiong, Institute of Computing Technology, Beijing, China

  • Venue:
  • EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
  • Year:
  • 2011

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Abstract

Dependency structure, as a first step towards semantics, is believed to be helpful to improve translation quality. However, previous works on dependency structure based models typically resort to insertion operations to complete translations, which make it difficult to specify ordering information in translation rules. In our model of this paper, we handle this problem by directly specifying the ordering information in head-dependents rules which represent the source side as head-dependents relations and the target side as strings. The head-dependents rules require only substitution operation, thus our model requires no heuristics or separate ordering models of the previous works to control the word order of translations. Large-scale experiments show that our model performs well on long distance reordering, and outperforms the state-of-the-art constituency-to-string model (+1.47 BLEU on average) and hierarchical phrase-based model (+0.46 BLEU on average) on two Chinese-English NIST test sets without resort to phrases or parse forest. For the first time, a source dependency structure based model catches up with and surpasses the state-of-the-art translation models.