Loria system for the wmt15 quality estimation shared task

Abstract

We describe our system for WMT2015 Shared Task on Quality Estimation, task 1, sentence-level prediction of post-edition effort. We use baseline features, Latent Semantic Indexing based features and features based on pseudo-references. SVM algorithm allows to estimate the linear regression between the features vectors and the HTER score. We use a selection algorithm in order to put aside needless features. Our best system leads to a performance in terms of Mean Absolute Error equal to 13.34 on official test while the official baseline system leads to a performance equal to 14.82.