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.
@article{arxiv.2210.10049,
title = {Alibaba-Translate China's Submission for WMT 2022 Quality Estimation Shared Task},
author = {Keqin Bao and Yu Wan and Dayiheng Liu and Baosong Yang and Wenqiang Lei and Xiangnan He and Derek F. Wong and Jun Xie},
journal= {arXiv preprint arXiv:2210.10049},
year = {2023}
}
Comments
WMT 2022 QE Shared Task. arXiv admin note: text overlap with arXiv:2210.09683