  Question answering from semi-structured tables can be seen as a semantic
parsing task and is significant and practical for pushing the boundary of
natural language understanding. Existing research mainly focuses on
understanding contents from unstructured evidence, e.g., news, natural language
sentences, and documents. The task of verification from structured evidence,
such as tables, charts, and databases, is still less explored. This paper
describes sattiy team's system in SemEval-2021 task 9: Statement Verification
and Evidence Finding with Tables (SEM-TAB-FACT). This competition aims to
verify statements and to find evidence from tables for scientific articles and
to promote the proper interpretation of the surrounding article. In this paper,
we exploited ensemble models of pre-trained language models over tables, TaPas
and TaBERT, for Task A and adjust the result based on some rules extracted for
Task B. Finally, in the leaderboard, we attain the F1 scores of 0.8496 and
0.7732 in Task A for the 2-way and 3-way evaluation, respectively, and the F1
score of 0.4856 in Task B.
