New Confidence Measures for Statistical Machine Translation
Sylvain Raybaud (INRIA Lorraine - LORIA), Caroline Lavecchia (INRIA Lorraine - LORIA), David Langlois (INRIA Lorraine - LORIA), Kamel Sma\"ili (INRIA Lorraine - LORIA)
Arxiv ID: 902.1033•Last updated: 2/9/2009
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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