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Quality Estimation, as a crucial step of quality control for machinetranslation, has been explored for years. The goal is to investigate automaticmethods for estimating the quality of machine translation results withoutreference translations. In this year's WMT QE shared task, we utilize thelarge-scale XLM-Roberta pre-trained model and additionally propose severaluseful features to evaluate the uncertainty of the translations to build our QEsystem, named \textit{QEMind}. The system has been applied to thesentence-level scoring task of Direct Assessment and the binary scoreprediction task of Critical Error Detection. In this paper, we present oursubmissions to the WMT 2021 QE shared task and an extensive set of experimentalresults have shown us that our multilingual systems outperform the best systemin the Direct Assessment QE task of WMT 2020.

QEMind: Alibaba's Submission to the WMT21 Quality Estimation Shared Task