Papers › A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task

A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task

9 Jun 2016ACL 2016 8arXiv:1606.02858archive 2025-07-28

Danqi Chen, Jason Bolton, Christopher D. Manning

Enabling a computer to understand a document so that it can answer comprehension questions is a central, yet unsolved goal of NLP. A key factor impeding its solution by machine learned systems is the limited availability of human-annotated data. Hermann et al. (2015) seek to solve this problem by creating over a million training examples by pairing CNN and Daily Mail news articles with their summarized bullet points, and show that a neural network can then be trained to give good performance on this task. In this paper, we conduct a thorough examination of this new reading comprehension task. Our primary aim is to understand what depth of language understanding is required to do well on this task. We approach this from one side by doing a careful hand-analysis of a small subset of the problems and from the other by showing that simple, carefully designed systems can obtain accuracies of 73.6% and 76.6% on these two datasets, exceeding current state-of-the-art results by 7-10% and approaching what we believe is the ceiling for performance on this task.

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danqi/rc-cnn-dailymail officialmentioned in papermentioned on GitHub report
clarenceguan/rc-cnn-daily-pytorch mentioned on GitHubpytorch report
clarenceguan/rc-cnn-dailymail-pytorch mentioned on GitHubpytorch report

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ArticlesReading Comprehension

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Question Answering CNN / Daily Mail Attentive + relabling + ensemble CNN 77.6 #3 of 16 Archive leaderboard report
Question Answering CNN / Daily Mail Attentive + relabling + ensemble Daily Mail 79.2 #3 of 16 Archive leaderboard report
Question Answering CNN / Daily Mail AttentiveReader + bilinear attention CNN 72.4 #11 of 16 Archive leaderboard report
Question Answering CNN / Daily Mail AttentiveReader + bilinear attention Daily Mail 75.8 #11 of 16 Archive leaderboard report
Question Answering CNN / Daily Mail Classifier CNN 67.9 #14 of 16 Archive leaderboard report
Question Answering CNN / Daily Mail Classifier Daily Mail 68.3 #14 of 16 Archive leaderboard report

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