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.

本文介绍了我们在 WMT 2020 Metrics Shared Task 中的贡献，通过扩展 BLEURT 度量标准评估 14 种语言对以及 4 种 ' 零样本 ' 语言对，针对英德语言对结合 BLEURT 和 YiSi 的预测结果进行性能增强，实验证据表明模型在 WMT Metrics 2019 Shared Task 中取得了有竞争力的结果，并展示了其在 2020 年版中的潜力。