  Recent methods based on pre-trained language models have exhibited superior
performance over tabular tasks (e.g., tabular NLI), despite showing inherent
problems such as not using the right evidence and inconsistent predictions
across inputs while reasoning over the tabular data. In this work, we utilize
Pattern-Exploiting Training (PET) (i.e., strategic MLM) on pre-trained language
models to strengthen these tabular reasoning models' pre-existing knowledge and
reasoning abilities. Our upgraded model exhibits a superior understanding of
knowledge facts and tabular reasoning compared to current baselines.
Additionally, we demonstrate that such models are more effective for underlying
downstream tasks of tabular inference on InfoTabs. Furthermore, we show our
model's robustness against adversarial sets generated through various character
and word level perturbations.
