Quality estimation from scratch (quetch): Deep learning forword-level translation quality estimation

Abstract

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 F1-scores for predicting word-level translation quality.