Towards Building a Personalized Dialogue Generator via Implicit User Persona Detection

Current works in the generation of personalized dialogue primarily contribute to the agent avoiding contradictory persona and driving the response more informative. However, we found that the generated responses from these models are mostly self-centered with little care for the other party since they ignore the user's persona. Moreover, we consider high-quality transmission is essentially built based on apprehending the persona of the other party. Motivated by this, we propose a novel personalized dialogue generator by detecting implicit user persona. Because it's difficult to collect a large number of personas for each user, we attempt to model the user's potential persona and its representation from the dialogue absence of any external information. Perception variable and fader variable are conceived utilizing Conditional Variational Inference. The two latent variables simulate the process of people being aware of the other party's persona and producing the corresponding expression in conversation. Finally, Posterior-discriminated Regularization is presented to enhance the training procedure. Empirical studies demonstrate that compared with the state-of-the-art methods, ours is more concerned with the user's persona and outperforms in evaluations.