Papers › Dynamic Coattention Networks For Question Answering

Dynamic Coattention Networks For Question Answering

5 Nov 2016arXiv:1611.01604archive 2025-07-28

Caiming Xiong, Victor Zhong, Richard Socher

Several deep learning models have been proposed for question answering. However, due to their single-pass nature, they have no way to recover from local maxima corresponding to incorrect answers. To address this problem, we introduce the Dynamic Coattention Network (DCN) for question answering. The DCN first fuses co-dependent representations of the question and the document in order to focus on relevant parts of both. Then a dynamic pointing decoder iterates over potential answer spans. This iterative procedure enables the model to recover from initial local maxima corresponding to incorrect answers. On the Stanford question answering dataset, a single DCN model improves the previous state of the art from 71.0% F1 to 75.9%, while a DCN ensemble obtains 80.4% F1.

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BAJUKA/SQuAD-NLP mentioned on GitHubtf report
Lou1sM/AML-Project mentioned on GitHubtf report
lmn-extracts/dcn_plus mentioned on GitHubtf report
wasimusu/MachineRC mentioned on GitHubpytorch report

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Tasks

DecoderQuestion Answering

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Open-Domain Question Answering SQuAD1.1 DCN EM 66.2 #2 of 3 Archive leaderboard report
Question Answering SQuAD1.1 Dynamic Coattention Networks (ensemble) EM 71.625 #148 of 213 Archive leaderboard report
Question Answering SQuAD1.1 Dynamic Coattention Networks (ensemble) F1 80.383 #148 of 213 Archive leaderboard report
Question Answering SQuAD1.1 Dynamic Coattention Networks (single model) EM 66.233 #178 of 213 Archive leaderboard report
Question Answering SQuAD1.1 Dynamic Coattention Networks (single model) F1 75.896 #178 of 213 Archive leaderboard report
Question Answering SQuAD1.1 dev DCN EM 65.4 #45 of 55 Archive leaderboard report
Question Answering SQuAD1.1 dev DCN F1 75.6 #45 of 55 Archive leaderboard report

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