  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.
