  In this report, we present our submission to the WMT 2022 Metrics Shared
Task. We build our system based on the core idea of UNITE (Unified Translation
Evaluation), which unifies source-only, reference-only, and
source-reference-combined evaluation scenarios into one single model.
Specifically, during the model pre-training phase, we first apply the
pseudo-labeled data examples to continuously pre-train UNITE. Notably, to
reduce the gap between pre-training and fine-tuning, we use data cropping and a
ranking-based score normalization strategy. During the fine-tuning phase, we
use both Direct Assessment (DA) and Multidimensional Quality Metrics (MQM) data
from past years' WMT competitions. Specially, we collect the results from
models with different pre-trained language model backbones, and use different
ensembling strategies for involved translation directions.
