  We present NewsQA, a challenging machine comprehension dataset of over
100,000 human-generated question-answer pairs. Crowdworkers supply questions
and answers based on a set of over 10,000 news articles from CNN, with answers
consisting of spans of text from the corresponding articles. We collect this
dataset through a four-stage process designed to solicit exploratory questions
that require reasoning. A thorough analysis confirms that NewsQA demands
abilities beyond simple word matching and recognizing textual entailment. We
measure human performance on the dataset and compare it to several strong
neural models. The performance gap between humans and machines (0.198 in F1)
indicates that significant progress can be made on NewsQA through future
research. The dataset is freely available at
https://datasets.maluuba.com/NewsQA.
