A Dynamic Strategy Coach for Effective Negotiation
September 30, 2019 ยท Declared Dead ยท ๐ SIGDIAL Conferences
"No code URL or promise found in abstract"
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Authors
Yiheng Zhou, He He, Alan W Black, Yulia Tsvetkov
arXiv ID
1909.13426
Category
cs.CL: Computation & Language
Citations
43
Venue
SIGDIAL Conferences
Last Checked
4 months ago
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 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%.
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