@InProceedings{potash-rumshisky:2017:EMNLP2017,
  author    = {Potash, Peter  and  Rumshisky, Anna},
  title     = {Towards Debate Automation: a Recurrent Model for Predicting Debate Winners},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {2465--2475},
  abstract  = {In this paper we introduce a practical first step towards the creation of an
	automated debate agent: a state-of-the-art recurrent predictive model for
	predicting debate winners. By having an accurate predictive model, we are able
	to objectively rate the quality of a statement made at a specific turn in a
	debate. The model is based on a recurrent neural network architecture with
	attention, which allows the model to effectively account for the entire debate
	when making its prediction. Our model achieves state-of-the-art accuracy on a
	dataset of debate transcripts annotated with audience favorability of the
	debate teams. Finally, we discuss how future work can leverage our proposed
	model for the creation of an automated debate agent. We accomplish this by
	determining the model input that will maximize audience favorability toward a
	given side of a debate at an arbitrary turn.},
  url       = {https://www.aclweb.org/anthology/D17-1261}
}