Exploring Early Prediction of Buyer-Seller Negotiation Outcomes

• URL: http://arxiv.org/abs/2004.02363v2
• Date: Fri, 26 Feb 2021 03:17:36 GMT
• Title: Exploring Early Prediction of Buyer-Seller Negotiation Outcomes
• Authors: Kushal Chawla, Gale Lucas, Jonathan May, Jonathan Gratch
• Abstract summary: 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.
• Score: 19.35826558501076
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. We further quantify the extent to which linguistic features help in making better predictions apart from the task-specific price information. Finally, probing the pretrained model helps us to identify specific features, such as trust and agreement, that contribute to the prediction performance.

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