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

ACL ID D11-1020
Title A novel dependency-to-string model for statistical machine translation
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2011
Authors

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 typi- cally resort to insertion operations to complete translations, which make it difficult to spec- ify 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 substitu- tion 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 l...