Bilingual Embeddings and Word Alignments for Translation Quality Estimation

This paper describes our submission UFAL MULTIVEC to the WMT16 Quality Estimation Shared Task, for English-German sentence-level post-editing effort prediction and ranking. Our approach exploits the power of bilingual distributed representations, word alignments and also manual post-edits to boost the performance of the baseline QuEst++ set of features. Our model outperforms the baseline, as well as the winning system in WMT15, Referential Translation Machines (RTM), in both scoring and ranking sub-tasks.