Source-language Features and Maximum Correlation Training for Machine Translation Evaluation

We propose three new features for MT evaluation: source-sentence constrained n-gram precision, source-sentence re-ordering metrics, and discriminative un-igram precision, as well as a method of learning linear feature weights to directly maximize correlation with human judg-ments. By aligning both the hypothe-sis and the reference with the source-language sentence, we achieve better cor-relation with human judgments than pre-viously proposed metrics. We further improve performance by combining indi-vidual evaluation metrics using maximum correlation training, which is shown to be better than the classification-based frame-work.