TY - GEN
T1 - A dynamic strategy coach for effective negotiation
AU - Zhou, Yiheng
AU - He, He
AU - Black, Alan W.
AU - Tsvetkov, Yulia
N1 - Publisher Copyright:
©2019 Association for Computational Linguistics
PY - 2019
Y1 - 2019
N2 - 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 dialog. Our negotiation coach monitors messages between them and recommends tactics in real time to the seller to get a better deal (e.g., “reject the proposal and propose a price”, “talk about your personal experience with the product”). The best strategy and tactics largely depend on the context (e.g., the current price, the buyer’s attitude). Therefore, we first identify a set of negotiation tactics, then learn to predict the best strategy and tactics in a given dialog context from a set of human–human bargaining dialogs. Evaluation on human–human dialogs shows that our coach increases the profits of the seller by almost 60%.1
AB - 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 dialog. Our negotiation coach monitors messages between them and recommends tactics in real time to the seller to get a better deal (e.g., “reject the proposal and propose a price”, “talk about your personal experience with the product”). The best strategy and tactics largely depend on the context (e.g., the current price, the buyer’s attitude). Therefore, we first identify a set of negotiation tactics, then learn to predict the best strategy and tactics in a given dialog context from a set of human–human bargaining dialogs. Evaluation on human–human dialogs shows that our coach increases the profits of the seller by almost 60%.1
UR - http://www.scopus.com/inward/record.url?scp=85091592627&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85091592627&partnerID=8YFLogxK
U2 - 10.18653/v1/w19-5943
DO - 10.18653/v1/w19-5943
M3 - Conference contribution
AN - SCOPUS:85091592627
T3 - SIGDIAL 2019 - 20th Annual Meeting of the Special Interest Group Discourse Dialogue - Proceedings of the Conference
SP - 367
EP - 378
BT - SIGDIAL 2019 - 20th Annual Meeting of the Special Interest Group Discourse Dialogue - Proceedings of the Conference
PB - Association for Computational Linguistics (ACL)
T2 - 20th Annual Meeting of the Special Interest Group on Discourse and Dialogue, SIGDIAL 2019
Y2 - 11 September 2019 through 13 September 2019
ER -