Machine Translation Evaluation with Textual Entailment Features
Sebastian Padó, Michel Galley, Daniel S. Jurafsky, Christopher D. Manning · Workshop on Statistical Machine Translation · 2009
We present two regression models for the prediction of pairwise preference judgments among MT hypotheses. Both models are based on feature sets that are motivated by textual entailment and incorporate lexical similarity as well as local syntactic features and specific semantic phenomena. One model predicts absolute scores; the other one direct pairwise judgments. We find that both models are competitive with regression models built over the scores of established MT evaluation metrics. Further data analysis clarifies the complementary behavior of the two feature sets.