  In the recent past, Natural language Inference (NLI) has gained significant
attention, particularly given its promise for downstream NLP tasks. However,
its true impact is limited and has not been well studied. Therefore, in this
paper, we explore the utility of NLI for one of the most prominent downstream
tasks, viz. Question Answering (QA). We transform the one of the largest
available MRC dataset (RACE) to an NLI form, and compare the performances of a
state-of-the-art model (RoBERTa) on both these forms. We propose new
characterizations of questions, and evaluate the performance of QA and NLI
models on these categories. We highlight clear categories for which the model
is able to perform better when the data is presented in a coherent entailment
form, and a structured question-answer concatenation form, respectively.
