Papers › ReasoNet: Learning to Stop Reading in Machine Comprehension
ReasoNet: Learning to Stop Reading in Machine Comprehension
Yelong Shen, Po-Sen Huang, Jianfeng Gao, Weizhu Chen
Teaching a computer to read and answer general questions pertaining to a document is a challenging yet unsolved problem. In this paper, we describe a novel neural network architecture called the Reasoning Network (ReasoNet) for machine comprehension tasks. ReasoNets make use of multiple turns to effectively exploit and then reason over the relation among queries, documents, and answers. Different from previous approaches using a fixed number of turns during inference, ReasoNets introduce a termination state to relax this constraint on the reasoning depth. With the use of reinforcement learning, ReasoNets can dynamically determine whether to continue the comprehension process after digesting intermediate results, or to terminate reading when it concludes that existing information is adequate to produce an answer. ReasoNets have achieved exceptional performance in machine comprehension datasets, including unstructured CNN and Daily Mail datasets, the Stanford SQuAD dataset, and a structured Graph Reachability dataset.
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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 | ReasoNet | CNN | 74.7 | #7 of 16 | Archive leaderboard | report |
| Question Answering | CNN / Daily Mail | ReasoNet | Daily Mail | 76.6 | #7 of 16 | Archive leaderboard | report |
| Question Answering | SQuAD1.1 | ReasoNet (ensemble) | EM | 75.034 | #123 of 213 | Archive leaderboard | report |
| Question Answering | SQuAD1.1 | ReasoNet (ensemble) | F1 | 82.552 | #123 of 213 | Archive leaderboard | report |
| Question Answering | SQuAD1.1 | ReasoNet (single model) | EM | 70.555 | #161 of 213 | Archive leaderboard | report |
| Question Answering | SQuAD1.1 | ReasoNet (single model) | F1 | 79.364 | #161 of 213 | 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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