We introduce the first fully automatic, fully semantic frame based MT evaluation metric, MEANT, that outperforms all other commonly used automatic metrics in correlating with human judgment on translation adequacy. Recent work on HMEANT, which is a human metric, indicates that machine translation can be better evaluated via semantic frames than other evaluation paradigms, requiring only minimal effort from monolingual humans to annotate and align semantic frames in the reference and machine translations. We propose a surprisingly effective Occam's razor automation of HMEANT that combines standard shallow semantic parsing with a simple maximum weighted bipartite matching algorithm for aligning semantic frames. The matching criterion is based on lexical similarity scoring of the semantic role fillers through a simple context vector model which can readily be trained using any publicly available large monolingual corpus. Sentence level correlation analysis, following standard NIST MetricsMATR protocol, shows that this fully automated version of HMEANT achieves significantly higher Kendall correlation with human adequacy judgments than BLEU, NIST, ME-TEOR, PER, CDER, WER, or TER. Furthermore, we demonstrate that performing the semantic frame alignment automatically actually tends to be just as good as performing it manually. Despite its high performance, fully automated MEANT is still able to preserve HMEANT's virtues of simplicity, representational transparency, and inexpensiveness.
REF proposed a semantic role driven MT metric.
Fully Automatic Semantic MT Evaluation
Proceedings of the Seventh Workshop on Statistical Machine Translation
Computer Science