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Alberto Sanchis, Alfons Juan, Enrique Vidal. Estimation of confidence measures for machine translation. Proceedings of the Machine Translation Summit XI, 2007. pp. 407-412.

Confidence Estimation has been extensively used in Speech Recognition and now it is also being applied in Statistical Machine Translation. Its basic goal is to estimate a confidence measure for each word in a given hypothesis, in order to locate those words, if any, that are likely to be incorrectly recognised or translated. It can be seen as a two-class pattern recognition problem in which each hypothesized word is transformed into a feature vector and then classified as either correct or incorrect. This view provides a solid, well-know framework, within which accurate dichotomizers (two-class classifiers) can be derived. In this paper, we study the performance of certain pattern features along with a smoothed Naive Bayes dichotomizer. Good empirical results are reported on a translation task of technical manuals.