TL
TL;DR
NewsQA, a challenging machine comprehension dataset of over 100,000 human-generated question-answer pairs, is presented and analysis confirms that NewsQA demands abilities beyond simple word matching and recognizing textual entailment.
Abstract
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 in the articles. We collect this dataset through a four-stage process designed to solicit exploratory questions that require reasoning. 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 (13.3% F1) indicates that significant progress can be made on NewsQA through future research. The dataset is freely available online.
References30 items
1
FastQA: A Simple and Efficient Neural Architecture for Question Answering
2
Multi-Perspective Context Matching for Machine Comprehension
3
Embracing data abundance: BookTest Dataset for Reading Comprehension
4
Machine Comprehension Using Match-LSTM and Answer Pointer
5