NUBIA: NeUral Based Interchangeability Assessor for Text Generation
Table 2: Absolute Pearson correlations with segment-level human judgments on WMT17 to-English translations. Correlations of metrics not significantly outperformed by any other for that language pair are highlighted in bold.
['[EMPTY]', '[BOLD] cs-en', '[BOLD] de-en', '[BOLD] fi-en', '[BOLD] lv-en', '[BOLD] ru-en', '[BOLD] tr-en', '[BOLD] zh-en', '[BOLD] AVG']
We compare our methods with methods developed for the WMT2017 challenge and models like RUSE and BERTScore which are currently the best performing methods. Although many methods have been proposed throughout the years in the WMT metrics challenge, the current methods used to this day to assess performance of summarization and translation models are still BLEU and ROUGE score. For ROUGE, we use ROUGE-L scores because it is the formulation of ROUGE correlated the most with human judgements on WMT 2017.