Paper: Source-Language Features and Maximum Correlation Training for Machine Translation Evaluation

ACL ID N07-1006
Title Source-Language Features and Maximum Correlation Training for Machine Translation Evaluation
Venue Human Language Technologies
Session Main Conference
Year 2007
Authors

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.