Learning dependency translation models as collections of finite-state head transducers
Computational Linguistics - Special issue on finite-state methods in NLP
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
Stochastic inversion transduction grammars and bilingual parsing of parallel corpora
Computational Linguistics
A syntax-based statistical translation model
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Discriminative training and maximum entropy models for statistical machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Learning to paraphrase: an unsupervised approach using multiple-sequence alignment
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
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
Loosely tree-based alignment for machine translation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Statistical Machine Translation with Scarce Resources Using Morpho-syntactic Information
Computational Linguistics
Machine Translation with Inferred Stochastic Finite-State Transducers
Computational Linguistics
A phrase-based, joint probability model for statistical machine translation
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
A projection extension algorithm for statistical machine translation
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Machine translation using probabilistic synchronous dependency insertion grammars
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
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We propose a novel syntax-based model for statistical machine translation in which meta-structure (MS) and meta-structure sequence (SMS) of a parse tree are defined. In this framework, a parse tree is decomposed into SMS to deal with the structure divergence and the alignment can be reconstructed at different levels of recombination of MS (RM). RM pairs extracted can perform the mapping between the sub-structures across languages. As a result, we have got not only the translation for the target language, but an SMS of its parse tree at the same time. Experiments with BLEU metric show that the model significantly outperforms Pharaoh, a state-art-the-art phrase-based system.