QUality Estimation from ScraTCH (QUETCH): Deep Learning for Word-level Translation Quality Estimation
Julia Kreutzer, Shigehiko Schamoni, Stefan Riezler · 2015
This paper describes the system submitted by the University of Heidelberg to the Shared Task on Word-level Quality Estimation at the 2015 Workshop on Statistical Machine Translation.The submitted system combines a continuous space deep neural network, that learns a bilingual feature representation from scratch, with a linear combination of the manually defined baseline features provided by the task organizers.A combination of these orthogonal information sources shows significant improvements over the combined systems, and produces very competitive F 1 -scores for predicting word-level translation quality.