  Translation suggestion (TS) models are used to automatically provide
alternative suggestions for incorrect spans in sentences generated by machine
translation. This paper introduces the system used in our submission to the
WMT'22 Translation Suggestion shared task. Our system is based on the ensemble
of different translation architectures, including Transformer, SA-Transformer,
and DynamicConv. We use three strategies to construct synthetic data from
parallel corpora to compensate for the lack of supervised data. In addition, we
introduce a multi-phase pre-training strategy, adding an additional
pre-training phase with in-domain data. We rank second and third on the
English-German and English-Chinese bidirectional tasks, respectively.
