Word embeddings and discourse information for Machine Translation Quality Estimation

Abstract

In this paper we present the results of the University of Sheffield (SHEF) submissions for the WMT16 shared task on document-level Quality Estimation (Task 3). Our submission explore discourse and document-aware information and word embeddings as features, with Support Vector Regression and Gaussian Process used to train the Quality Estimation models. The use of word embeddings (combined with baseline features) and a Gaussian Process model with two kernels led to the winning submission in the shared task.