  We explore the task of improving persona consistency of dialogue agents.
Recent models tackling consistency often train with additional Natural Language
Inference (NLI) labels or attach trained extra modules to the generative agent
for maintaining consistency. However, such additional labels and training can
be demanding. Also, we find even the best-performing persona-based agents are
insensitive to contradictory words. Inspired by social cognition and
pragmatics, we endow existing dialogue agents with public self-consciousness on
the fly through an imaginary listener. Our approach, based on the Rational
Speech Acts framework (Frank and Goodman, 2012), can enforce dialogue agents to
refrain from uttering contradiction. We further extend the framework by
learning the distractor selection, which has been usually done manually or
randomly. Results on Dialogue NLI (Welleck et al., 2019) and PersonaChat (Zhang
et al., 2018) dataset show that our approach reduces contradiction and improves
consistency of existing dialogue models. Moreover, we show that it can be
generalized to improve context-consistency beyond persona in dialogues.
