  This paper presents the team TransQuest's participation in Sentence-Level
Direct Assessment shared task in WMT 2020. We introduce a simple QE framework
based on cross-lingual transformers, and we use it to implement and evaluate
two different neural architectures. The proposed methods achieve
state-of-the-art results surpassing the results obtained by OpenKiwi, the
baseline used in the shared task. We further fine tune the QE framework by
performing ensemble and data augmentation. Our approach is the winning solution
in all of the language pairs according to the WMT 2020 official results.
