  Existing approaches to constructing training data for Natural Language
Inference (NLI) tasks, such as for semi-structured table reasoning, are either
via crowdsourcing or fully automatic methods. However, the former is expensive
and time-consuming and thus limits scale, and the latter often produces naive
examples that may lack complex reasoning. This paper develops a realistic
semi-automated framework for data augmentation for tabular inference. Instead
of manually generating a hypothesis for each table, our methodology generates
hypothesis templates transferable to similar tables. In addition, our framework
entails the creation of rational counterfactual tables based on human written
logical constraints and premise paraphrasing. For our case study, we use the
InfoTabs, which is an entity-centric tabular inference dataset. We observed
that our framework could generate human-like tabular inference examples, which
could benefit training data augmentation, especially in the scenario with
limited supervision.
