@InProceedings{persing-ng:2017:I17-1,
  author    = {Persing, Isaac  and  Ng, Vincent},
  title     = {Lightly-Supervised Modeling of Argument Persuasiveness},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {594--604},
  abstract  = {We propose the first lightly-supervised approach to scoring an argument's
	persuasiveness. Key to our approach is the novel hypothesis that
	lightly-supervised persuasiveness scoring is possible by explicitly modeling
	the major errors that negatively impact persuasiveness. In an evaluation on a
	new annotated corpus of online debate arguments, our approach rivals its
	fully-supervised counterparts in performance by four scoring metrics when using
	only 10% of the available training instances.},
  url       = {http://www.aclweb.org/anthology/I17-1060}
}