@InProceedings{potash-bhattacharya-rumshisky:2017:I17-1,
  author    = {Potash, Peter  and  Bhattacharya, Robin  and  Rumshisky, Anna},
  title     = {Length, Interchangeability, and External Knowledge: Observations from Predicting Argument Convincingness},
  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     = {342--351},
  abstract  = {In this work, we provide insight into three key aspects related to predicting
	argument convincingness. First, we explicitly display the power that text
	length possesses for predicting convincingness in an unsupervised setting.
	Second, we show that a bag-of-words embedding model posts state-of-the-art
	on a dataset of arguments annotated for convincingness, outperforming an
	SVM with numerous hand-crafted features as well as recurrent neural network
	models that attempt to capture semantic composition. Finally, we assess
	the feasibility of integrating external knowledge when predicting
	convincingness, as arguments are often more convincing when they contain
	abundant information and facts. We finish by analyzing the correlations
	between the various models we propose.},
  url       = {http://www.aclweb.org/anthology/I17-1035}
}