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In this paper, we present our submission to the sentence-level MQM benchmarkat Quality Estimation Shared Task, named UniTE (Unified TranslationEvaluation). Specifically, our systems employ the framework of UniTE, whichcombined three types of input formats during training with a pre-trainedlanguage model. First, we apply the pseudo-labeled data examples for thecontinuously pre-training phase. Notably, to reduce the gap betweenpre-training and fine-tuning, we use data pruning and a ranking-based scorenormalization strategy. For the fine-tuning phase, we use both DirectAssessment (DA) and Multidimensional Quality Metrics (MQM) data from pastyears' WMT competitions. Finally, we collect the source-only evaluationresults, and ensemble the predictions generated by two UniTE models, whosebackbones are XLM-R and InfoXLM, respectively. Results show that our modelsreach 1st overall ranking in the Multilingual and English-Russian settings, and2nd overall ranking in English-German and Chinese-English settings, showingrelatively strong performances in this year's quality estimation competition.

Alibaba-Translate China's Submission for WMT 2022 Quality Estimation Shared Task