SHEF-Lite 2.0: Sparse Multi-task Gaussian Processes for Translation Quality Estimation

By Daniel Beck, Kashif Shah, Lucia Specia. 2014

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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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Probabilistic & Bayesian Methods, Machine Translation