Automatic Stance Detection Using End-to-End Memory Networks

Mitra Mohtarami, Ramy Baly, James Glass, Preslav Nakov, Lluís Màrquez, Alessandro Moschitti


Abstract
We present an effective end-to-end memory network model that jointly (i) predicts whether a given document can be considered as relevant evidence for a given claim, and (ii) extracts snippets of evidence that can be used to reason about the factuality of the target claim. Our model combines the advantages of convolutional and recurrent neural networks as part of a memory network. We further introduce a similarity matrix at the inference level of the memory network in order to extract snippets of evidence for input claims more accurately. Our experiments on a public benchmark dataset, FakeNewsChallenge, demonstrate the effectiveness of our approach.
Anthology ID:
N18-1070
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venues:
HLT | NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
767–776
URL:
https://www.aclweb.org/anthology/N18-1070
DOI:
10.18653/v1/N18-1070
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