Word embeddings and discourse information for Quality Estimation
Carolina Scarton, Daniel Beck, Kashif Ur Rehman Shah, Karin Smith, Lucia Specia · 2016
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.