  Quality Estimation (QE) of Machine Translation (MT) is a task to estimate the
quality scores for given translation outputs from an unknown MT system.
However, QE scores for low-resource languages are usually intractable and hard
to collect. In this paper, we focus on the Sentence-Level QE Shared Task of the
Fifth Conference on Machine Translation (WMT20), but in a more challenging
setting. We aim to predict QE scores of given translation outputs when barely
none of QE scores of that paired languages are given during training. We
propose an ensemble-based predictor-estimator QE model with transfer learning
to overcome such QE data scarcity challenge by leveraging QE scores from other
miscellaneous languages and translation results of targeted languages. Based on
the evaluation results, we provide a detailed analysis of how each of our
extension affects QE models on the reliability and the generalization ability
to perform transfer learning under multilingual tasks. Finally, we achieve the
best performance on the ensemble model combining the models pretrained by
individual languages as well as different levels of parallel trained corpus
with a Pearson's correlation of 0.298, which is 2.54 times higher than
baselines.
