@InProceedings{stab-gurevych:2017:EACLlong,
  author    = {Stab, Christian  and  Gurevych, Iryna},
  title     = {Recognizing Insufficiently Supported Arguments in Argumentative Essays},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {980--990},
  abstract  = {In this paper, we propose a new task for assessing the quality of natural
	language arguments. The premises of a well-reasoned argument should provide
	enough evidence for accepting or rejecting its claim. Although this criterion,
	known as sufficiency, is widely adopted in argumentation theory, there are no
	empirical studies on its applicability to real arguments. In this work, we show
	that human annotators substantially agree on the sufficiency criterion and
	introduce a novel annotated corpus. Furthermore, we experiment with
	feature-rich SVMs and Convolutional Neural Networks and achieve 84% accuracy
	for automatically identifying insufficiently supported arguments. The final
	corpus as well as the annotation guideline are freely available for encouraging
	future research on argument quality.},
  url       = {http://www.aclweb.org/anthology/E17-1092}
}