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Conference Papers Year : 2009

New Confidence Measures for Statistical Machine Translation

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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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Dates and versions

inria-00333843 , version 1 (30-01-2009)


  • 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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