UAlacant word-level and phrase-level machine translation quality estimation systems at WMT 2016

This paper describes the Universitat d’Alacant submissions (labeled as UAlacant) to the machine translation quality estimation (MTQE) shared task at WMT 2016, where we have participated in the word-level and phrase-level MTQE subtasks. Our systems use external sources of bilingual information as a black box to spot sub-segment correspondences between the source segment and the translation hypothesis. For our submissions, two sources of bilingual information have been used: machine translation (Lucy LT KWIK Translator and Google Translate) and the bilingual concordancer Reverso Context. Building upon the word-level approach implemented for WMT 2015, a method for phrase-based MTQE is proposed which builds on the probabilities obtained for word-level MTQE. For each sub-task we have submitted two systems: one using the features produced exclusively based on online sources of bilingual information, and one combining them with the baseline features provided by the organisers of the task.