Current works in the generation of personalized dialogue primarily contribute
to the agent presenting a consistent personality and driving a more informative
response. However, we found that the generated responses from most previous
models tend to be self-centered, with little care for the user in the dialogue.
Moreover, we consider that human-like conversation is essentially built based
on inferring information about the persona of the other party. Motivated by
this, we propose a novel personalized dialogue generator by detecting an
implicit user persona. Because it is hard to collect a large number of detailed
personas for each user, we attempted to model the user's potential persona and
its representation from dialogue history, with no external knowledge. The
perception and fader variables were conceived using conditional variational
inference. The two latent variables simulate the process of people being aware
of each other's persona and producing a corresponding expression in
conversation. Finally, posterior-discriminated regularization was presented to
enhance the training procedure. Empirical studies demonstrate that, compared to
state-of-the-art methods, our approach is more concerned with the user's
persona and achieves a considerable boost across the evaluations.

提出了一种基于用户人格检测的个性化对话生成器，该方法使用条件变分推理建立用户潜在人格模型并利用后验鉴别正则化提高训练效果，在实证研究中实现比现有技术更高的对用户人格关注和提升了对话品质的效果。