Currently end-to-end deep learning based open-domain dialogue systems remain
black box models, making it easy to generate irrelevant contents with
data-driven models. Specifically, latent variables are highly entangled with
different semantics in the latent space due to the lack of priori knowledge to
guide the training. To address this problem, this paper proposes to harness the
generative model with a priori knowledge through a cognitive approach involving
mesoscopic scale feature disentanglement. Particularly, the model integrates
the macro-level guided-category knowledge and micro-level open-domain dialogue
data for the training, leveraging the priori knowledge into the latent space,
which enables the model to disentangle the latent variables within the
mesoscopic scale. Besides, we propose a new metric for open-domain dialogues,
which can objectively evaluate the interpretability of the latent space
distribution. Finally, we validate our model on different datasets and
experimentally demonstrate that our model is able to generate higher quality
and more interpretable dialogues than other models.

本研究旨在通过介绍一种认知方法，利用先前的知识驱动生成模型，实现在中尺度特征分离中调节潜在变量，使得深度学习的开放领域对话系统可以生成更高质量和更可解释性的对话。

高级条件变分自编码器（A-CVAE）：通过解离潜在特征表示进行开放领域对话生成解释的探索

Advanced Conditional Variational Autoencoders (A-CVAE): Towards interpreting open-domain conversation generation via disentangling latent feature representation