Quality Estimation (QE) plays an essential role in applications of Machine Translation (MT). Traditionally, a QE system accepts the original source text and translation from a black-box MT system as input. Recently, a few studies indicate that as a by-product of translation, QE benefits from the model and training datas information of the MT system where the translations come from, and it is called the glass-box QE. In this paper, we extend the definition of glass-box QE generally to uncertainty quantification with both black-box and glass-box approaches and design several features deduced from them to blaze a new trial in improving QEs performance. We propose a framework to fuse the feature engineering of uncertainty quantification into a pre-trained cross-lingual language model to predict the translation quality. Experiment results show that our method achieves state-of-the-art performances on the datasets of WMT 2020 QE shared task.
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