Quality estimation is an essential step in applying machine translation systems in practice, however state-of-the-art approaches require manual post-edits and other expensive resources. We introduce an approach to quality estimation that uses the attention weights of a neural machine translation system and can be applied to a translation produced by any machine translation system; a lighter version of the approach does not even require any post-edits. Our experiments with German-Estonian and English-Estonian translations show that its performance matches the state-of-the-art baseline.
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