2020

Exploring Early Prediction of Buyer-Seller Negotiation Outcomes

Chawla, Kushal, Lucas, Gale, May, Jonathan et al.

Understand

Agents that negotiate with humans find broad applications in pedagogy and conversational AI.

Most efforts in human-agent negotiations rely on restrictive menu-driven interfaces for communication.
To advance the research in language-based negotiation systems, we explore a novel task of early prediction of buyer-seller negotiation outcomes, by varying the fraction of utterances that the model can access.
We explore the feasibility of early prediction by using traditional feature-based methods, as well as by incorporating the non-linguistic task context into a pretrained language model using sentence templates.

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