Unbabel's Participation in the WMT17 Translation Quality Estimation Shared Task

摘要

This paper presents the contribution of the Unbabel team to the WMT 2017 Shared Task on Translation Quality Estimation. We participated on the word-level and sentence-level tracks. We describe our two submitted systems: (1) StackedQE, a "pure" QE system, trained only on the provided training sets, which is a stacked combination of a feature-rich sequential linear model with a neural network, and (ii) FULLSTACKEDQE, which also stacks the predictions of an automatic post-editing system, trained on additional data. When evaluated on the English-German and German-English datasets, FULLSTACKEDQE achieved word-level F_1~(MULT) scores of 56.6% and 52 9%, and sentence-level correlation Pearson scores of 64.1% and 62.6%, respectively. Our system ranked second in both tracks, being statistically indistinguishable from the best system in the word-level track.