@InProceedings{cocarascu-toni:2017:EMNLP2017,
  author    = {Cocarascu, Oana  and  Toni, Francesca},
  title     = {Identifying attack and support argumentative relations using deep learning},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {1374--1379},
  abstract  = {We propose a deep learning architecture to capture argumentative relations of
	attack and support from one piece of text to another, of the kind that
	naturally occur in a debate. The architecture uses two (unidirectional or
	bidirectional) Long Short-Term Memory networks and (trained or non-trained)
	word embeddings, and allows to considerably improve upon existing techniques
	that use syntactic features and supervised classifiers for the same form of
	(relation-based) argument mining.},
  url       = {https://www.aclweb.org/anthology/D17-1144}
}