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.
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This [[Category::paper]] presents a statistical phrase-based [[AddressesProblem::Machine Translation|machine translation]] model that uses hierarchical phrases (phrases that contain subphrases). The model is ''formally'' syntax-based because it uses [[UsesMethod::Synchronous Context-Free Grammars]] but not ''linguistically'' syntax-based because the grammar is learned from a parallel text without using any linguistic annotations or assumptions.
  
 
== Experimental results ==
 
== Experimental results ==
  
 
== Related papers ==
 
== Related papers ==

Revision as of 16:38, 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 presents a statistical phrase-based machine translation model that uses hierarchical phrases (phrases that contain subphrases). The model is formally syntax-based because it uses Synchronous Context-Free Grammars but not linguistically syntax-based because the grammar is learned from a parallel text without using any linguistic annotations or assumptions.

Experimental results

Related papers