New Confidence Measures for Statistical Machine Translation

Sylvain Raybaud 1 Caroline Lavecchia 1 David Langlois 1 Kamel Smaïli 1
1 PAROLE - Analysis, perception and recognition of speech
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : A confidence measure is able to estimate the reliability of an hypothesis provided by a machine translation system. The problem of confidence measure can be seen as a process of testing : we want to decide whether the most probable sequence of words provided by the machine translation system is correct or not. In the following we describe several original word-level confidence measures for machine translation, based on mutual information, n-gram language model and lexical features language model. We evaluate how well they perform individually or together, and show that using a combination of confidence measures based on mutual information yields a classification error rate as low as 25.1\% with an F-measure of 0.708.
Type de document :
Communication dans un congrès
International Conference On Agents and Artificial Intelligence - ICAART 09, Jan 2009, Porto, Portugal. 2009, Proceedings of the International Conference On Agents and Artificial Intelligence
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https://hal.inria.fr/inria-00333843
Contributeur : Sylvain Raybaud <>
Soumis le : vendredi 30 janvier 2009 - 15:59:45
Dernière modification le : lundi 24 septembre 2018 - 09:04:03
Document(s) archivé(s) le : lundi 7 juin 2010 - 18:58:17

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  • HAL Id : inria-00333843, version 1
  • ARXIV : 0902.1033

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Sylvain Raybaud, Caroline Lavecchia, David Langlois, Kamel Smaïli. New Confidence Measures for Statistical Machine Translation. International Conference On Agents and Artificial Intelligence - ICAART 09, Jan 2009, Porto, Portugal. 2009, Proceedings of the International Conference On Agents and Artificial Intelligence. 〈inria-00333843〉

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