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Quality Estimation (QE) plays an essential role in applications of MachineTranslation (MT). Traditionally, a QE system accepts the original source textand translation from a black-box MT system as input. Recently, a few studiesindicate that as a by-product of translation, QE benefits from the model andtraining data's 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 toblaze a new trial in improving QE's performance. We propose a framework to fusethe feature engineering of uncertainty quantification into a pre-trainedcross-lingual language model to predict the translation quality. Experimentresults show that our method achieves state-of-the-art performances on thedatasets of WMT 2020 QE shared task.

Beyond Glass-Box Features: Uncertainty Quantification Enhanced Quality Estimation for Neural Machine Translation