Estimation of Confidence Measures for Machine Translation

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-known 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.