Difference between revisions of "Chiang 2005"
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== Summary == | == Summary == | ||
| − | 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. Using BLEU as a metric, | + | 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. Using BLEU as a metric, it is shown to outperform previous state-of-the-art phrase-based systems. |
| + | |||
| + | The hierarchical model is motivated by the inability of conventional phrase-based models to learn reorderings of phrases (and not only local reorderings of words). For example, considering the following Mandarin sentence: | ||
| + | Aozhou shi yu Bei Han you bangjiao de shaoshu guojia zhiyi | ||
| + | Australia is with North Korea have diplomatic relations that few countries one of | ||
| + | the typical output of a conventional phrase-based system would be: | ||
| + | Australia is diplomatic relations with North Korea is one of the few countries | ||
== Experimental results == | == Experimental results == | ||
== Related papers == | == Related papers == | ||
Revision as of 15:56, 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. Using BLEU as a metric, it is shown to outperform previous state-of-the-art phrase-based systems.
The hierarchical model is motivated by the inability of conventional phrase-based models to learn reorderings of phrases (and not only local reorderings of words). For example, considering the following Mandarin sentence:
Aozhou shi yu Bei Han you bangjiao de shaoshu guojia zhiyi Australia is with North Korea have diplomatic relations that few countries one of
the typical output of a conventional phrase-based system would be:
Australia is diplomatic relations with North Korea is one of the few countries
