Papers › Attention-over-Attention Neural Networks for Reading Comprehension

Attention-over-Attention Neural Networks for Reading Comprehension

15 Jul 2016ACL 2017 7arXiv:1607.04423archive 2025-07-28

Yiming Cui, Zhipeng Chen, Si Wei, Shijin Wang, Ting Liu, Guoping Hu

Cloze-style queries are representative problems in reading comprehension. Over the past few months, we have seen much progress that utilizing neural network approach to solve Cloze-style questions. In this paper, we present a novel model called attention-over-attention reader for the Cloze-style reading comprehension task. Our model aims to place another attention mechanism over the document-level attention, and induces "attended attention" for final predictions. Unlike the previous works, our neural network model requires less pre-defined hyper-parameters and uses an elegant architecture for modeling. Experimental results show that the proposed attention-over-attention model significantly outperforms various state-of-the-art systems by a large margin in public datasets, such as CNN and Children's Book Test datasets.

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Code

OlavHN/attention-over-attention mentioned on GitHubtf report
kevinkwl/AoAReader mentioned on GitHubpytorch report

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Tasks

Question AnsweringReading Comprehension

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Question Answering CNN / Daily Mail AoA Reader CNN 74.4 #8 of 16 Archive leaderboard report
Question Answering Children's Book Test AoA reader Accuracy-CN 69.4% #3 of 8 Archive leaderboard report
Question Answering Children's Book Test AoA reader Accuracy-NE 72% #3 of 8 Archive leaderboard report

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