Improving Dialog Systems for Negotiation with Personality Modeling

• URL: http://arxiv.org/abs/2010.09954v2
• Date: Mon, 21 Jun 2021 06:28:57 GMT
• Title: Improving Dialog Systems for Negotiation with Personality Modeling
• Authors: Runzhe Yang, Jingxiao Chen, Karthik Narasimhan
• Abstract summary: 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.
• Score: 30.78850714931678
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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