Expected dependency pair match: predicting translation quality with expected syntactic structure

摘要

Recent efforts to develop new machine translation evaluation methods have tried to account for allowable wording differences either in terms of syntactic structure or synonyms/paraphrases. This paper primarily considers syntactic structure, combining scores from partial syntactic dependency matches with standard local n-gram matches using a statistical parser, and taking advantage of N-best parse probabilities. The new scoring metric, expected dependency pair match (EDPM), is shown to outperform BLEU and TER in terms of correlation to human judgments and as a predictor of HTER. Further, we combine the syntactic features of EDPM with the alternative wording features of TERp, showing a benefit to accounting for syntactic structure on top of semantic equivalency features.