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

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