Difference between revisions of "Chiang 2005"
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== Summary == | == Summary == | ||
| − | This [[Category::paper]] describes a statistical phrase-based [[AddressesProblem:Machine Translation|machine translation]] model that uses hierarchical phrases (phrases that contain subphrases). The model is formally a [[UsesMethod::Synchronous Context-Free Grammars|synchronous context-free grammar]] but is learned from a bitext without any syntactic information. Using BLEU as a metric, the hierarchical phrase based model achieves a relative improvement of 7.5% over Pharaoh, a state-of-the-art phrase-based system. | + | This [[Category::paper]] describes a statistical phrase-based [[AddressesProblem::Machine Translation|machine translation]] model that uses hierarchical phrases (phrases that contain subphrases). The model is formally a [[UsesMethod::Synchronous Context-Free Grammars|synchronous context-free grammar]] but is learned from a bitext without any syntactic information. Using BLEU as a metric, the hierarchical phrase based model achieves a relative improvement of 7.5% over Pharaoh, a state-of-the-art phrase-based system. |
== Experimental results == | == Experimental results == | ||
== Related papers == | == Related papers == | ||
Revision as of 09:03, 1 November 2011
Citation
Chiang, D. 2005. A Hierarchical Phrase-Based Model for Statistical Machine Translation. In Proceedings of the 43rd Annual Meeting of the ACL, pp. 263–270, Ann Arbor. Association for Computational Linguistics.
Online version
Information Sciences Institute, University of Southern California
Summary
This paper describes a statistical phrase-based machine translation model that uses hierarchical phrases (phrases that contain subphrases). The model is formally a synchronous context-free grammar but is learned from a bitext without any syntactic information. Using BLEU as a metric, the hierarchical phrase based model achieves a relative improvement of 7.5% over Pharaoh, a state-of-the-art phrase-based system.
