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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.
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https://hal.inria.fr/inria-00333843
Contributor : Sylvain Raybaud <>
Submitted on : Friday, January 30, 2009 - 3:59:45 PM
Last modification on : Monday, September 24, 2018 - 9:04:03 AM
Document(s) archivé(s) le : Monday, June 7, 2010 - 6:58:17 PM

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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. ⟨inria-00333843⟩

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