@InProceedings{mohtarami-EtAl:2018:N18-1,
  author    = {Mohtarami, Mitra  and  Baly, Ramy  and  Glass, James  and  Nakov, Preslav  and  Màrquez, Lluís  and  Moschitti, Alessandro},
  title     = {Automatic Stance Detection Using End-to-End Memory Networks},
  booktitle = {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},
  publisher = {Association for Computational Linguistics},
  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.},
  url       = {http://www.aclweb.org/anthology/N18-1070}
}