We describe our systems for the WMT14 Shared Task on Quality Estimation (subtasks 1.1, 1.2 and 1.3). Our submissions use the framework of Multi-task Gaussian Processes, where we combine multiple datasets in a multi-task setting. Due to the large size of our datasets we also experiment with Sparse Gaussian Processes, which aim to speed up training and prediction by providing sensible sparse approximations.
['Daniel Beck', 'Kashif Shah', 'Lucia Specia']
SHEF-Lite 2.0: Sparse Multi-task Gaussian Processes for Translation Quality Estimation