A polynomial-time algorithm for statistical machine translation
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
A syntax-based statistical translation model
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Statistical phrase-based translation
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
On the parameter space of generative lexicalized statistical parsing models
On the parameter space of generative lexicalized statistical parsing models
A hierarchical phrase-based model for statistical machine translation
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Maximum entropy based phrase reordering model for statistical machine translation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Tree-to-string alignment template for statistical machine translation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Moses: open source toolkit for statistical machine translation
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
Head-modifier relation based non-lexical reordering model for phrase-based translation
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
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The complicated alignment and small translation unit make the word based approaches extremely complex and thereby hard to achieve promising performance. The employment of phrase largely addresses the alignment problem. On the other hand, the phrase-based SMT (PBSMT) models suffer more from data sparse problem and behave less flexible than word-based model because of the larger translation unit --phrase. Therefore we conduct our research on enhancing phrase based SMT with word-level reordering model (based on source dependency tree). Experimental results on the NIST Chinese-English machine translation data show that our reordering models significantly improve the baseline, a state-of-the-art reordering model, which is widely used in phrase-based SMT system.