@InProceedings{beigmanklebanov-gyawali-song:2017:Short,
  author    = {Beigman Klebanov, Beata  and  Gyawali, Binod  and  Song, Yi},
  title     = {Detecting Good Arguments in a Non-Topic-Specific Way: An Oxymoron?},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {244--249},
  abstract  = {Automatic identification of good arguments on a controversial topic has
	applications in civics and education, to name a few. While in the civics
	context it might be acceptable to create separate models for  each topic, in
	the context of              scoring of students' writing there is a preference for a
	single
	model that applies to all responses. Given that good arguments for one topic
	are likely to be irrelevant for another, is a single model for detecting good
	arguments a contradiction in terms? We investigate the extent to which it is
	possible to close the performance gap between topic-specific and across-topics
	models for identification of good arguments.},
  url       = {http://aclweb.org/anthology/P17-2038}
}