  Creating challenging tabular inference data is essential for learning complex
reasoning. Prior work has mostly relied on two data generation strategies. The
first is human annotation, which yields linguistically diverse data but is
difficult to scale. The second category for creation is synthetic generation,
which is scalable and cost effective but lacks inventiveness. In this research,
we present a framework for semi-automatically recasting existing tabular data
to make use of the benefits of both approaches. We utilize our framework to
build tabular NLI instances from five datasets that were initially intended for
tasks like table2text creation, tabular Q/A, and semantic parsing. We
demonstrate that recasted data could be used as evaluation benchmarks as well
as augmentation data to enhance performance on tabular NLI tasks. Furthermore,
we investigate the effectiveness of models trained on recasted data in the
zero-shot scenario, and analyse trends in performance across different recasted
datasets types.
