Papers › Natural Language Comprehension with the EpiReader
Natural Language Comprehension with the EpiReader
Adam Trischler, Zheng Ye, Xingdi Yuan, Kaheer Suleman
We present the EpiReader, a novel model for machine comprehension of text. Machine comprehension of unstructured, real-world text is a major research goal for natural language processing. Current tests of machine comprehension pose questions whose answers can be inferred from some supporting text, and evaluate a model's response to the questions. The EpiReader is an end-to-end neural model comprising two components: the first component proposes a small set of candidate answers after comparing a question to its supporting text, and the second component formulates hypotheses using the proposed candidates and the question, then reranks the hypotheses based on their estimated concordance with the supporting text. We present experiments demonstrating that the EpiReader sets a new state-of-the-art on the CNN and Children's Book Test machine comprehension benchmarks, outperforming previous neural models by a significant margin.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Question Answering | CNN / Daily Mail | EpiReader | CNN | 74 | #9 of 16 | Archive leaderboard | report |
| Question Answering | Children's Book Test | EpiReader | Accuracy-CN | 67.4% | #7 of 8 | Archive leaderboard | report |
| Question Answering | Children's Book Test | EpiReader | Accuracy-NE | 69.7% | #7 of 8 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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