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We present a supervised learning pilot application for estimating Machine Translation (MT) output reusability, in view of supporting a human post-editor of MT content. We train our model on typed dependencies (labeled grammar relationships) extracted from human reference and raw MT data, to then predict grammar relationship correctness values that we aggregate to provide a binary segmentlevel evaluation. In view of scaling up to larger data, we provide implemented Naïve Bayes and Stochasticdoi:10.3115/v1/w14-0303 dblp:conf/eacl/KirkZG14 fatcat:qlr6hhr3jbhhdcod72qwhiyqj4