Title: NEWSQA: A MACHINE COMPREHENSION DATASET
Authors: Adam Trischler, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, Kaheer Suleman
Keywords: Natural language processing, Deep learning
TL;DR: Crowdsourced QA dataset with natural language questions and multi-word answers
Abstract:
We present NewsQA, a challenging machine comprehension dataset of over 100,000 question-answer pairs. Crowdworkers supply questions and answers based on a set of over 10,000 news articles from CNN, with answers consisting in 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 entailment. We measure human performance on the dataset and compare it to several strong neural models. The performance gap between humans and machines (25.3% F1) indicates that significant progress can be made on NewsQA through future research. The dataset is freely available at datasets.maluuba.com/NewsQA.
Venue: Submitted to ICLR 2017