@inproceedings{zhou-etal-2019-dynamic,
    title = "A Dynamic Strategy Coach for Effective Negotiation",
    author = "Zhou, Yiheng  and
      He, He  and
      Black, Alan W  and
      Tsvetkov, Yulia",
    editor = "Nakamura, Satoshi  and
      Gasic, Milica  and
      Zukerman, Ingrid  and
      Skantze, Gabriel  and
      Nakano, Mikio  and
      Papangelis, Alexandros  and
      Ultes, Stefan  and
      Yoshino, Koichiro",
    booktitle = "Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue",
    month = sep,
    year = "2019",
    address = "Stockholm, Sweden",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/W19-5943/",
    doi = "10.18653/v1/W19-5943",
    pages = "367--378",
    abstract = "Negotiation is a complex activity involving strategic reasoning, persuasion, and psychology. An average person is often far from an expert in negotiation. Our goal is to assist humans to become better negotiators through a machine-in-the-loop approach that combines machine`s advantage at data-driven decision-making and human`s language generation ability. We consider a bargaining scenario where a seller and a buyer negotiate the price of an item for sale through a text-based dialogue. Our negotiation coach monitors messages between them and recommends strategies in real time to the seller to get a better deal (e.g., {\textquotedblleft}reject the proposal and propose a price{\textquotedblright}, {\textquotedblleft}talk about your personal experience with the product{\textquotedblright}). The best strategy largely depends on the context (e.g., the current price, the buyer`s attitude). Therefore, we first identify a set of negotiation strategies, then learn to predict the best strategy in a given dialogue context from a set of human-human bargaining dialogues. Evaluation on human-human dialogues shows that our coach increases the profits of the seller by almost 60{\%}."
}