@InProceedings{sobhani-inkpen-zhu:2017:EACLshort,
  author    = {Sobhani, Parinaz  and  Inkpen, Diana  and  Zhu, Xiaodan},
  title     = {A Dataset for Multi-Target Stance Detection},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {551--557},
  abstract  = {Current models for stance classification often treat each target independently,
	but in many applications, there exist natural dependencies among targets, e.g.,
	stance towards two or more politicians in an election or towards several brands
	of the same product. In this paper, we focus on the problem of multi-target
	stance detection. We present a new dataset that we built for this task.
	Furthermore, We experiment with several neural models on the dataset and show
	that they are more effective in jointly modeling the overall position towards
	two related targets compared to independent predictions and other models of
	joint learning, such as cascading classification. We make the new dataset
	publicly available, in order to facilitate further research in multi-target
	stance classification.},
  url       = {http://www.aclweb.org/anthology/E17-2088}
}