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., “reject the proposal and propose a price”, “talk about your personal experience with the product”). 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%.- Anthology ID:
- W19-5943
- Volume:
- Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue
- Month:
- September
- Year:
- 2019
- Address:
- Stockholm, Sweden
- Venue:
- SIGDIAL
- SIG:
- SIGDIAL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 367–378
- Language:
- URL:
- https://aclanthology.org/W19-5943
- DOI:
- 10.18653/v1/W19-5943
- Cite (ACL):
- Yiheng Zhou, He He, Alan W Black, and Yulia Tsvetkov. 2019. A Dynamic Strategy Coach for Effective Negotiation. In Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue, pages 367–378, Stockholm, Sweden. Association for Computational Linguistics.
- Cite (Informal):
- A Dynamic Strategy Coach for Effective Negotiation (Zhou et al., SIGDIAL 2019)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/W19-5943.pdf