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Current works in the generation of personalized dialogue primarily contributeto the agent presenting a consistent personality and driving a more informativeresponse. However, we found that the generated responses from most previousmodels tend to be self-centered, with little care for the user in the dialogue.Moreover, we consider that human-like conversation is essentially built basedon inferring information about the persona of the other party. Motivated bythis, we propose a novel personalized dialogue generator by detecting animplicit user persona. Because it is hard to collect a large number of detailedpersonas for each user, we attempted to model the user's potential persona andits representation from dialogue history, with no external knowledge. Theperception and fader variables were conceived using conditional variationalinference. The two latent variables simulate the process of people being awareof each other's persona and producing a corresponding expression inconversation. Finally, posterior-discriminated regularization was presented toenhance the training procedure. Empirical studies demonstrate that, compared tostate-of-the-art methods, our approach is more concerned with the user'spersona and achieves a considerable boost across the evaluations.

A Personalized Dialogue Generator with Implicit User Persona Detection