SHEF-MIME:Word-level Quality Estimation Using Imitation Learning

Abstract

We describe University of Sheffield's submission to the word-level Quality Estimation shared task. Our system is based on imitation learning, an approach to structured prediction which relies on a classifier trained on data generated appropriately to ameliorate error propagation. Compared to other structure prediction approaches such as conditional random fields, it allows the use of arbitrary information from previous tag predictions and the use of non-decomposable loss functions over the structure. We explore these two aspects in our submission while using the baseline features provided by the shared task organisers. Our system outperformed the conditional random field baseline while using the same feature set.