  Translation Suggestion (TS), which provides alternatives for specific words
or phrases given the entire documents translated by machine translation (MT)
\cite{lee2021intellicat}, has been proven to play a significant role in post
editing (PE). However, there is still no publicly available data set to support
in-depth research for this problem, and no reproducible experimental results
can be followed by researchers in this community. To break this limitation, we
create a benchmark data set for TS, called \emph{WeTS}, which contains golden
corpus annotated by expert translators on four translation directions. Apart
from the human-annotated golden corpus, we also propose several novel methods
to generate synthetic corpus which can substantially improve the performance of
TS. With the corpus we construct, we introduce the Transformer-based model for
TS, and experimental results show that our model achieves State-Of-The-Art
(SOTA) results on all four translation directions, including English-to-German,
German-to-English, Chinese-to-English and English-to-Chinese. Codes and corpus
can be found at https://github.com/ZhenYangIACAS/WeTS.git.
