Improving Dialog Systems for Negotiation with Personality Modeling

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 (He et al., 2018) and show that our method using ToM inference achieves a 20% higher dialog agreement rate compared to baselines on a mixed population of opponents.

Developing dialog systems for negotiation is challenging since the task requires a combination of good communication skills and strategic reasoning capabilities (Traum et al., 2008; Young et al., 2013; Keizer et al., 2017). While recent neural models (Wen et al., 2017; Dhingra et al., 2017; Zhou et al., 2019; He et al., 2018) have shown that useful dialogue strategies can be learned from offline corpora, they do not explicitly model the mental state of other agents, which can make it challenging to generate tailored strategies and utterances for different types of opponents.

To emulate this capability in machines, we train a firstorder ToM model to predict an opponent’s response given the current state and the agent’s own possible utterances. This first-order ToM model can then be incorporated into dialog agents to enable one-step lookaheads during inference.