  Tables are widely used in various kinds of documents to present information
concisely. Understanding tables is a challenging problem that requires an
understanding of language and table structure, along with numerical and logical
reasoning. In this paper, we present our systems to solve Task 9 of
SemEval-2021: Statement Verification and Evidence Finding with Tables
(SEM-TAB-FACTS). The task consists of two subtasks: (A) Given a table and a
statement, predicting whether the table supports the statement and (B)
Predicting which cells in the table provide evidence for/against the statement.
We fine-tune TAPAS (a model which extends BERT's architecture to capture
tabular structure) for both the subtasks as it has shown state-of-the-art
performance in various table understanding tasks. In subtask A, we evaluate how
transfer learning and standardizing tables to have a single header row improves
TAPAS' performance. In subtask B, we evaluate how different fine-tuning
strategies can improve TAPAS' performance. Our systems achieve an F1 score of
67.34 in subtask A three-way classification, 72.89 in subtask A two-way
classification, and 62.95 in subtask B.
