  In this paper, we present our submission to the sentence-level MQM benchmark
at Quality Estimation Shared Task, named UniTE (Unified Translation
Evaluation). Specifically, our systems employ the framework of UniTE, which
combined three types of input formats during training with a pre-trained
language model. First, we apply the pseudo-labeled data examples for the
continuously pre-training phase. Notably, to reduce the gap between
pre-training and fine-tuning, we use data pruning and a ranking-based score
normalization strategy. For the fine-tuning phase, we use both Direct
Assessment (DA) and Multidimensional Quality Metrics (MQM) data from past
years' WMT competitions. Finally, we collect the source-only evaluation
results, and ensemble the predictions generated by two UniTE models, whose
backbones are XLM-R and InfoXLM, respectively. Results show that our models
reach 1st overall ranking in the Multilingual and English-Russian settings, and
2nd overall ranking in English-German and Chinese-English settings, showing
relatively strong performances in this year's quality estimation competition.
