  Reasoning about tabular information presents unique challenges to modern NLP
approaches which largely rely on pre-trained contextualized embeddings of text.
In this paper, we study these challenges through the problem of tabular natural
language inference. We propose easy and effective modifications to how
information is presented to a model for this task. We show via systematic
experiments that these strategies substantially improve tabular inference
performance.
