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Many Question-Answering (QA) datasets contain unanswerable questions, buttheir treatment in QA systems remains primitive. Our analysis of the NaturalQuestions (Kwiatkowski et al. 2019) dataset reveals that a substantial portionof unanswerable questions ($\sim$21%) can be explained based on the presence ofunverifiable presuppositions. We discuss the shortcomings of current models inhandling such questions, and describe how an improved system could handle them.Through a user preference study, we demonstrate that the oracle behavior of ourproposed system that provides responses based on presupposition failure ispreferred over the oracle behavior of existing QA systems. Then we discuss howour proposed system could be implemented, presenting a novel framework thatbreaks down the problem into three steps: presupposition generation,presupposition verification and explanation generation. We report our progressin tackling each subproblem, and present a preliminary approach to integratingthese steps into an existing QA system. We find that adding presuppositions andtheir verifiability to an existing model yields modest gains in downstreamperformance and unanswerability detection. The biggest bottleneck is theverification component, which needs to be substantially improved for theintegrated system to approach ideal behavior -- even transfer from the bestentailment models currently falls short.

Which Linguist Invented the Lightbulb? Presupposition Verification for Question-Answering