@InProceedings{noseworthy-cheung-pineau:2017:W17-55,
  author    = {Noseworthy, Michael  and  Cheung, Jackie Chi Kit  and  Pineau, Joelle},
  title     = {Predicting Success in Goal-Driven Human-Human Dialogues},
  booktitle = {Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue},
  month     = {August},
  year      = {2017},
  address   = {Saarbrücken, Germany},
  publisher = {Association for Computational Linguistics},
  pages     = {253--262},
  abstract  = {In goal-driven dialogue systems, success is often defined based on a structured
	definition of the goal. This requires that the dialogue system be constrained
	to handle a specific class of goals and that there be a mechanism to measure
	success with respect to that goal. However, in many human-human dialogues the
	diversity of goals makes it infeasible to define success in such a way. To
	address this scenario, we consider the task of automatically predicting success
	in goal-driven human-human dialogues using only the information communicated
	between participants in the form of text. We build a dataset from
	stackoverflow.com which consists of exchanges between two users in the
	technical domain where ground-truth success labels are available. We then
	propose a turn-based hierarchical neural network model that can be used to
	predict success without requiring a structured goal definition. We show this
	model outperforms rule-based heuristics and other baselines as it is able to
	detect patterns over the course of a dialogue and capture notions such as
	gratitude.},
  url       = {http://aclweb.org/anthology/W17-5531}
}