Automatic Stance Detection Using End-to-End Memory Networks

NAACL 2018 Mitra MohtaramiRamy BalyJames GlassPreslav NakovLluis MarquezAlessandro 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... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT LEADERBOARD
Fake News Detection FNC-1 Neural method from Mohtarami et al. + TF-IDF (Mohtarami et al., 2018) Weighted Accuracy 81.23 # 4
Fake News Detection FNC-1 Neural method from Mohtarami et al. (Mohtarami et al., 2018) Weighted Accuracy 78.97 # 5