In this paper, we present our submission to Shared Metrics Task: RoBLEURT (Robustly Optimizing the training of BLEURT). After investigating the recent advances of trainable metrics, we conclude several aspects of vital importance to obtain a well-performed metric model by: 1) jointly leveraging the advantages of source-included model and reference-only model, 2) continuously pre-training the model with massive synthetic data pairs, and 3) fine-tuning the model with data denoising strategy. Experimental results show that our model reaching state-of-the-art correlations with the WMT2020 human annotations upon 8 out of 10 to-English language pairs.

翻译：在本文中,我们介绍我们提交共同计量任务的文件:RobleurT(大力优化BLEURT的培训)。在调查了可培训指标的最新进展之后,我们得出了几个至关重要的方面,以便获得一个完善的衡量模型,即:1) 共同利用源载模型和仅参考模型的优势,2) 不断以大规模合成数据对子对模型进行预先培训,3) 以数据脱色战略对模型进行微调。实验结果显示,我们的模式在10至英语对子的8种语言中达到了与WMT2020人类说明的最新相关性。