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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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Contributor : Sylvain Raybaud Connect in order to contact the contributor
Submitted on : Friday, January 30, 2009 - 3:59:45 PM
Last modification on : Friday, February 26, 2021 - 3:28:05 PM
Long-term archiving on: : Monday, June 7, 2010 - 6:58:17 PM


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



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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