@InProceedings{lewis-EtAl:2017:EMNLP2017,
  author    = {Lewis, Mike  and  Yarats, Denis  and  Dauphin, Yann  and  Parikh, Devi  and  Batra, Dhruv},
  title     = {Deal or No Deal? End-to-End Learning of Negotiation Dialogues},
  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     = {2443--2453},
  abstract  = {Much of human dialogue occurs in semi-cooperative settings, where agents with
	different goals attempt to agree on common decisions. Negotiations require
	complex communication and reasoning skills, but success is easy to measure,
	making this an interesting task for AI. We gather a large dataset of
	human-human negotiations on a multi-issue bargaining task, where agents who
	cannot observe each other’s reward functions must reach an agreement (or a
	deal) via natural language dialogue. For the first time, we show it is possible
	to train end-to-end models for negotiation, which must learn both linguistic
	and reasoning skills with no annotated dialogue states. We also introduce
	dialogue rollouts, in which the model plans ahead by simulating possible
	complete continuations of the conversation, and find that this technique
	dramatically improves performance. Our code and dataset are publicly available.},
  url       = {https://www.aclweb.org/anthology/D17-1259}
}