The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th 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
Cognates can improve statistical translation models
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
Minimum error rate training in statistical machine translation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
A hierarchical phrase-based model for statistical machine translation
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Hierarchical Phrase-Based Translation
Computational Linguistics
A systematic comparison of phrase-based, hierarchical and syntax-augmented statistical MT
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Toward smaller, faster, and better hierarchical phrase-based SMT
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Bucking the trend: large-scale cost-focused active learning for statistical machine translation
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Feature-rich discriminative phrase rescoring for SMT
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Hybrid decoding: decoding with partial hypotheses combination over multiple SMT systems
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
| Hi-index | 0.00 |
In this paper, we propose a novel method of reducing the size of translation model for hierarchical phrase-based machine translation systems. Previous approaches try to prune infrequent entries or unreliable entries based on statistics, but cause a problem of reducing the translation coverage. On the contrary, the proposed method try to prune only ineffective entries based on the estimation of the information redundancy encoded in phrase pairs and hierarchical rules, and thus preserve the search space of SMT decoders as much as possible. Experimental results on Chinese-to-English machine translation tasks show that our method is able to reduce almost the half size of the translation model with very tiny degradation of translation performance.