Synchronous tree-adjoining grammars
COLING '90 Proceedings of the 13th conference on Computational linguistics - Volume 3
A syntax-based statistical translation model
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
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
Effective phrase translation extraction from alignment models
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Learning non-isomorphic tree mappings for machine translation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 2
The Alignment Template Approach to Statistical Machine Translation
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 hierarchical phrase-based model for statistical machine translation
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Dependency treelet translation: syntactically informed phrasal SMT
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Machine translation using probabilistic synchronous dependency insertion grammars
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
Scalable inference and training of context-rich syntactic translation models
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Reordering constraints for phrase-based statistical machine translation
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Local phrase reordering models for statistical machine translation
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Hierarchical Phrase-Based Translation
Computational Linguistics
SPMT: statistical machine translation with syntactified target language phrases
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
A unigram orientation model for statistical machine translation
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Parsing '05 Proceedings of the Ninth International Workshop on Parsing Technology
Post-ordering by parsing for Japanese-English statistical machine translation
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
Post-Ordering by Parsing with ITG for Japanese-English Statistical Machine Translation
ACM Transactions on Asian Language Information Processing (TALIP)
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Phrase reordering is of great importance for statistical machine translation. According to the movement of phrase translation, the pattern of phrase reordering can be divided into three classes: monotone, BTG (Bracket Transduction Grammar) and hierarchy. It is a good way to use different styles of reordering models to reorder different phrases according to the characteristics of both the reordering models and phrases itself. In this paper a novel reordering model based on multi-layer phrase (PRML) is proposed, where the source sentence is segmented into different layers of phrases on which different reordering models are applied to get the final translation. This model has some advantages: different styles of phrase reordering models are easily incorporated together; when a complicated reordering model is employed, it can be limited in a smaller scope and replaced with an easier reordering model in larger scope. So this model better trade-offs the translation speed and performance simultaneously.