@inproceedings{mohtarami-etal-2018-automatic,
title = "Automatic Stance Detection Using End-to-End Memory Networks",
author = "Mohtarami, Mitra and
Baly, Ramy and
Glass, James and
Nakov, Preslav and
M{\`a}rquez, Llu{\'\i}s and
Moschitti, Alessandro",
editor = "Walker, Marilyn and
Ji, Heng and
Stent, Amanda",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-1070",
doi = "10.18653/v1/N18-1070",
pages = "767--776",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Automatic Stance Detection Using End-to-End Memory Networks
%A Mohtarami, Mitra
%A Baly, Ramy
%A Glass, James
%A Nakov, Preslav
%A Màrquez, Lluís
%A Moschitti, Alessandro
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F mohtarami-etal-2018-automatic
%X 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.
%R 10.18653/v1/N18-1070
%U https://aclanthology.org/N18-1070
%U https://doi.org/10.18653/v1/N18-1070
%P 767-776
Markdown (Informal)
[Automatic Stance Detection Using End-to-End Memory Networks](https://aclanthology.org/N18-1070) (Mohtarami et al., NAACL 2018)
ACL
- Mitra Mohtarami, Ramy Baly, James Glass, Preslav Nakov, Lluís Màrquez, and Alessandro Moschitti. 2018. Automatic Stance Detection Using End-to-End Memory Networks. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 767–776, New Orleans, Louisiana. Association for Computational Linguistics.