  This paper describes our submission of the WMT 2020 Shared Task on Sentence
Level Direct Assessment, Quality Estimation (QE). In this study, we empirically
reveal the \textit{mismatching issue} when directly adopting BERTScore to QE.
Specifically, there exist lots of mismatching errors between the source
sentence and translated candidate sentence with token pairwise similarity. In
response to this issue, we propose to expose explicit cross-lingual patterns,
\textit{e.g.} word alignments and generation score, to our proposed zero-shot
models. Experiments show that our proposed QE model with explicit cross-lingual
patterns could alleviate the mismatching issue, thereby improving the
performance. Encouragingly, our zero-shot QE method could achieve comparable
performance with supervised QE method, and even outperforms the supervised
counterpart on 2 out of 6 directions. We expect our work could shed light on
the zero-shot QE model improvement.
