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.Comment: NAACL-2018; Stance detection; Fact-Checking; Veracity; Memory
networks; Neural Networks; Distributed Representation