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

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 . * Work done during an internship at Google. Repetition When is Eas ugh Easter this year? Correction When is Lent I meant Easter this year? Restarts How much no wait when is Easter this year? (a) Conventional categories of Disfluencies. The reparandum (words intended to be corrected or ignored), interregnum (optional discourse cues) and repair are marked.