BERT in Negotiations: Early Prediction of Buyer-Seller Negotiation Outcomes
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The task of building automatic agents that can negotiate with humans in free-form natural language has gained recent interest in the literature. Although there have been initial attempts, combining linguistic understanding with strategy effectively still remains a challenge. Towards this end, we aim to understand the role of natural language in negotiations from a data-driven perspective by attempting to predict a negotiation's outcome, well before the negotiation is complete. Building on the recent advancements in pre-trained language encoders, our model is able to predict correctly within 10 than 70 suggest that rather than just being a way to realize a negotiation, natural language should be incorporated in the negotiation planning as well. Such a framework can be directly used to get feedback for training an automatically negotiating agent.
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