We introduce referential translation machines (RTM) for quality estimation of translation outputs. RTMs are a computational model for identifying the translation acts between any two data sets with respect to a reference corpus selected in the same domain, which can be used for estimating the quality of translation outputs, judging the semantic similarity between text, and evaluating the quality of student answers. RTMs achieve top performance in automatic, accurate, and language independent prediction of udsentence-level and word-level statistical machine translation (SMT) quality. RTMs remove the need to access any SMT system specific information or prior knowledge of the training data or models used when generating the translations. We develop novel techniques for solving all subtasks in the WMT13 quality estimation (QE) task (QET 2013) based on individual RTM models. Our results achieve improvements over last year’s QE task results (QET 2012), as well as our previous results, provide new features and techniques for QE, and rank 1st or 2nd in all of the subtasks.
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