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