Papers › Automatic Stance Detection Using End-to-End Memory Networks
Automatic Stance Detection Using End-to-End Memory Networks
Mitra Mohtarami, Ramy Baly, James Glass, Preslav Nakov, Lluis Marquez, Alessandro Moschitti
We present a novel end-to-end memory network for stance detection, which jointly (i) predicts whether a document agrees, disagrees, discusses or is unrelated with respect to a given target claim, and also (ii) extracts snippets of evidence for that prediction. The network operates at the paragraph level and integrates convolutional and recurrent neural networks, as well as a similarity matrix as part of the overall architecture. The experimental evaluation on the Fake News Challenge dataset shows state-of-the-art performance.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Fake News Detection | FNC-1 | Neural method from Mohtarami et al. + TF-IDF (Mohtarami et al., 2018) | Weighted Accuracy | 81.23 | #6 of 10 | Archive leaderboard | report |
| Fake News Detection | FNC-1 | Neural method from Mohtarami et al. (Mohtarami et al., 2018) | Weighted Accuracy | 78.97 | #7 of 10 | Archive leaderboard | report |
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Methods
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