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The quality of machine translation systems has dramatically improved over thelast decade, and as a result, evaluation has become an increasingly challengingproblem. This paper describes our contribution to the WMT 2020 Metrics SharedTask, the main benchmark for automatic evaluation of translation. We makeseveral submissions based on BLEURT, a previously published metric based ontransfer learning. We extend the metric beyond English and evaluate it on 14language pairs for which fine-tuning data is available, as well as 4"zero-shot" language pairs, for which we have no labelled examples.Additionally, we focus on English to German and demonstrate how to combineBLEURT's predictions with those of YiSi and use alternative referencetranslations to enhance the performance. Empirical results show that the modelsachieve competitive results on the WMT Metrics 2019 Shared Task, indicatingtheir promise for the 2020 edition.

Learning to Evaluate Translation Beyond English: BLEURT Submissions to the WMT Metrics 2020 Shared Task