Public Self-consciousness for Endowing Dialogue Agents with Consistent Persona

Although consistency has been a long-standing issue in dialogue agents, we show best-performing persona-conditioned generative models still suffer from high insensitivity to contradiction. Current approaches for improving consistency rely on supervised external models and labels which are demanding. Inspired by social cognition and pragmatics, we model public self-consciousness in dialogue agents through an imaginary listener to improve consistency. Our approach, based on the Rational Speech Acts framework (Frank & Goodman, 2012), attempts to maintain consistency in an unsupervised manner requiring neither additional annotations nor pretrained external models. We further extend the framework by learning the distractor supply for the first time. Experimental results show that our approach effectively reduces contradiction and improves consistency on Dialogue NLI (Welleck et al., 2019) and PersonaChat (Zhang et al., 2018).