@InProceedings{habernal-EtAl:2017:EMNLP2017Demos,
  author    = {Habernal, Ivan  and  Hannemann, Raffael  and  Pollak, Christian  and  Klamm, Christopher  and  Pauli, Patrick  and  Gurevych, Iryna},
  title     = {Argotario: Computational Argumentation Meets Serious Games},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {7--12},
  abstract  = {An important skill in critical thinking and argumentation is the ability to
	spot and recognize fallacies. Fallacious arguments, omnipresent in
	argumentative discourse, can be deceptive, manipulative, or simply leading to
	'wrong moves' in a discussion. Despite their importance, argumentation scholars
	and NLP researchers with focus on argumentation quality have not yet
	investigated fallacies empirically. The nonexistence of resources dealing with
	fallacious argumentation calls for scalable approaches to data acquisition and
	annotation, for which the serious games methodology offers an appealing, yet
	unexplored, alternative. We present Argotario, a serious game that deals with
	fallacies in everyday argumentation. Argotario is a multilingual, open-source,
	platform-independent application with strong educational aspects, accessible at
	www.argotario.net.},
  url       = {http://www.aclweb.org/anthology/D17-2002}
}