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arXiv · 2010.09954

Improving Dialog Systems for Negotiation with Personality Modeling

Runzhe Yang·Jingxiao Chen·Karthik Narasimhan
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Abstract

In this paper, we explore the ability to model and infer personality types of opponents, predict their responses, and use this information to adapt a dialog agent's high-level strategy in negotiation tasks. Inspired by the idea of incorporating a theory of mind (ToM) into machines, we introduce a probabilistic formulation to encapsulate the opponent's personality type during both learning and inference. We test our approach on the CraigslistBargain dataset and show that our method using ToM inference achieves a 20% higher dialog agreement rate compared to baselines on a mixed population of opponents. We also find that our model displays diverse negotiation behavior with different types of opponents.

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Runzhe Yang, Jingxiao Chen, Karthik Narasimhan. 2020-10-20. Improving Dialog Systems for Negotiation with Personality Modeling. https://arxiv.org/abs/2010.09954

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