BiMEANT: Integrating Cross-Lingual and Monolingual Semantic Frame Similarities in the MEANT Semantic MT Evaluation Metric

Abstract

We present experimental results showing that integrating cross-lingual semantic frame similarity into the semantic frame based automatic MT evaluation metric MEANT improves its correlation with human judgment on evaluating translation adequacy. Recent work shows that MEANT more accurately reflects translation adequacy than other automatic MT evaluation metrics such as BLEU or TER, and that moreover, optimizing SMT systems against MEANT robustly improves translation quality across different output languages. However, in some cases the human reference translation employs different scoping strategies from the input sentence and thus standard monolingual MEANT, which only assesses translation quality via the semantic frame similarity between the reference and machine translations, fails to fairly and accurately reward the adequacy of the machine translation. To address this issue we propose a new bilingual metric, BiMEANT, that correlates with human judgment more closely than MEANT by incorporating new cross-lingual semantic frame similarity assessments into MEANT.KeywordsFrame SemanticsMachine TranslationReference TranslationAssessing Translation QualityRole FillersThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.