  Translation quality evaluation plays a crucial role in machine translation.
According to the input format, it is mainly separated into three tasks, i.e.,
reference-only, source-only and source-reference-combined. Recent methods,
despite their promising results, are specifically designed and optimized on one
of them. This limits the convenience of these methods, and overlooks the
commonalities among tasks. In this paper, we propose UniTE, which is the first
unified framework engaged with abilities to handle all three evaluation tasks.
Concretely, we propose monotonic regional attention to control the interaction
among input segments, and unified pretraining to better adapt multi-task
learning. We testify our framework on WMT 2019 Metrics and WMT 2020 Quality
Estimation benchmarks. Extensive analyses show that our \textit{single model}
can universally surpass various state-of-the-art or winner methods across
tasks. Both source code and associated models are available at
https://github.com/NLP2CT/UniTE.
