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The dependency between an adequate question formulation and correct answerselection is a very intriguing but still underexplored area. In this paper, weshow that question rewriting (QR) of the conversational context allows to shedmore light on this phenomenon and also use it to evaluate robustness ofdifferent answer selection approaches. We introduce a simple framework thatenables an automated analysis of the conversational question answering (QA)performance using question rewrites, and present the results of this analysison the TREC CAsT and QuAC (CANARD) datasets. Our experiments uncoversensitivity to question formulation of the popular state-of-the-art models forreading comprehension and passage ranking. Our results demonstrate that thereading comprehension model is insensitive to question formulation, while thepassage ranking changes dramatically with a little variation in the inputquestion. The benefit of QR is that it allows us to pinpoint and group suchcases automatically. We show how to use this methodology to verify whether QAmodels are really learning the task or just finding shortcuts in the dataset,and better understand the frequent types of error they make.

A Wrong Answer or a Wrong Question? An Intricate Relationship between  Question Reformulation and Answer Selection in Conversational Question  Answering