  In this paper we define and investigate the problem of \emph{persona
authentication}: learning a conversational policy to verify the consistency of
persona models. We propose a learning objective and prove (under some mild
assumptions) that local density estimators trained under this objective
maximize the mutual information between persona information and dialog
trajectory. Based on the proposed objective, we develop a method of learning an
authentication model that adaptively outputs personalized questions to reveal
the underlying persona of its partner throughout the course of multi-turn
conversation. Experiments show that our authentication method discovers
effective question sequences that generalize to unseen persona profiles.
