  Quality Estimation, as a crucial step of quality control for machine
translation, has been explored for years. The goal is to investigate automatic
methods for estimating the quality of machine translation results without
reference translations. In this year's WMT QE shared task, we utilize the
large-scale XLM-Roberta pre-trained model and additionally propose several
useful features to evaluate the uncertainty of the translations to build our QE
system, named \textit{QEMind}. The system has been applied to the
sentence-level scoring task of Direct Assessment and the binary score
prediction task of Critical Error Detection. In this paper, we present our
submissions to the WMT 2021 QE shared task and an extensive set of experimental
results have shown us that our multilingual systems outperform the best system
in the Direct Assessment QE task of WMT 2020.
