  The quality of machine translation systems has dramatically improved over the
last decade, and as a result, evaluation has become an increasingly challenging
problem. This paper describes our contribution to the WMT 2020 Metrics Shared
Task, the main benchmark for automatic evaluation of translation. We make
several submissions based on BLEURT, a previously published metric based on
transfer learning. We extend the metric beyond English and evaluate it on 14
language 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 combine
BLEURT's predictions with those of YiSi and use alternative reference
translations to enhance the performance. Empirical results show that the models
achieve competitive results on the WMT Metrics 2019 Shared Task, indicating
their promise for the 2020 edition.
