  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.
