Word- and sentence-level confidence measures for machine translation

Sylvain Raybaud 1, * Caroline Lavecchia 1 David Langlois 1 Kamel Smaïli 1
* Auteur correspondant
1 PAROLE - Analysis, perception and recognition of speech
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : A machine translated sentence is seldom completely correct. Confidence measures are designed to detect incorrect words, phrases or sentences, or to provide an estimation of the probability of correctness. In this article we describe several word- and sentence-level confidence measures relying on different features: mutual information between words, n-gram and backward n-gram language models, and linguistic features. We also try different combination of these measures. Their accuracy is evaluated on a classification task. We achieve 17% error-rate (0.84 f-measure) on word-level and 31% error-rate (0.71 f-measure) on sentence-level.
Keywords : confidence measure machine translation mutual information
Type de document :
Communication dans un congrès
13th Annual Meeting of the European Association for Machine Translation - EAMT 09, May 2009, Barcelona, Spain. 2009, Proceedings of the 13th Annual Meeting of the European Association for Machine Translation - EAMT 09
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Soumis le : mercredi 16 septembre 2009 - 10:43:29
Dernière modification le : jeudi 11 janvier 2018 - 06:19:56
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Sylvain Raybaud, Caroline Lavecchia, David Langlois, Kamel Smaïli. Word- and sentence-level confidence measures for machine translation. 13th Annual Meeting of the European Association for Machine Translation - EAMT 09, May 2009, Barcelona, Spain. 2009, Proceedings of the 13th Annual Meeting of the European Association for Machine Translation - EAMT 09. 〈inria-00417541〉

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