NewsQA: A Machine Comprehension Dataset
- Adam Trischler ,
- Tong Wang ,
- Xingdi (Eric) Yuan ,
- Justin D. Harris ,
- Alessandro Sordoni ,
- Philip Bachman ,
- Kaheer Suleman
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.
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NewsQA Dataset
7 3 月, 2018
The purpose of Microsoft Montreal's NewsQA dataset is to help the research community build algorithms that are capable of answering questions requiring human-level comprehension and reasoning skills. Leveraging CNN articles from the DeepMind Q&A Dataset, we prepared a crowd-sourced machine reading comprehension dataset of 120K Q&A pairs.