  Translation quality estimation (TQE) is the task of predicting translation
quality without reference translations. Due to the enormous cost of creating
training data for TQE, only a few translation directions can benefit from
supervised training. To address this issue, unsupervised TQE methods have been
studied. In this paper, we extensively investigate the usefulness of synthetic
TQE data and pre-trained multilingual encoders in unsupervised sentence-level
TQE, both of which have been proven effective in the supervised training
scenarios. Our experiment on WMT20 and WMT21 datasets revealed that this
approach can outperform other unsupervised TQE methods on high- and
low-resource translation directions in predicting post-editing effort and human
evaluation score, and some zero-resource translation directions in predicting
post-editing effort.
