@InProceedings{xu-EtAl:2018:Short2,
  author    = {Xu, Chang  and  Paris, Cecile  and  Nepal, Surya  and  Sparks, Ross},
  title     = {Cross-Target Stance Classification with Self-Attention Networks},
  booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Australia},
  publisher = {Association for Computational Linguistics},
  pages     = {778--783},
  abstract  = {In stance classification, the target on which the stance is made defines the boundary of the task, and a classifier is usually trained for prediction on the same target. In this work, we explore the potential for generalizing classifiers between different targets, and propose a neural model that can apply what has been learned from a source target to a destination target. We show that our model can find useful information shared between relevant targets which improves generalization in certain scenarios.},
  url       = {http://www.aclweb.org/anthology/P18-2123}
}