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Disfluencies is an under-studied topic in NLP, even though it is ubiquitousin human conversation. This is largely due to the lack of datasets containingdisfluencies. In this paper, we present a new challenge question answeringdataset, Disfl-QA, a derivative of SQuAD, where humans introduce contextualdisfluencies in previously fluent questions. Disfl-QA contains a variety ofchallenging disfluencies that require a more comprehensive understanding of thetext than what was necessary in prior datasets. Experiments show that theperformance of existing state-of-the-art question answering models degradessignificantly when tested on Disfl-QA in a zero-shot setting.We show dataaugmentation methods partially recover the loss in performance and alsodemonstrate the efficacy of using gold data for fine-tuning. We argue that weneed large-scale disfluency datasets in order for NLP models to be robust tothem. The dataset is publicly available at:https://github.com/google-research-datasets/disfl-qa.

Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering