Conditional Variational AutoEncoder (CVAE) is promising for modeling one-to-many relationships in dialogue generation, as it can naturally generate many responses from a given context. However, the conventional used continual latent variables in CVAE are more likely to generate generic rather than distinct and specific responses. To resolve this problem, we introduce a novel discrete variable called prior context which enables the generation of favorable responses. Specifically, we present Prior Context VAE (PCVAE), a hierarchical VAE that learns prior context from data automatically for dialogue generation. Meanwhile, we design Active Codeword Transport (ACT) to help the model actively discover potential prior context. Moreover, we propose Autoregressive Compatible Arrangement (ACA) that enables modeling prior context in autoregressive style, which is crucial for selecting appropriate prior context according to a given context. Extensive experiments demonstrate that PCVAE can generate distinct responses and significantly outperforms strong baselines.

Modern recommender systems are increasingly expected to provide informative explanations that enable users to understand the reason for particular recommendations. However, previous methods struggle to interpret the input IDs of user--item pairs in real-world datasets, failing to extract adequate characteristics for controllable generation. To address this issue, we propose disentangled conditional variational autoencoders (CVAEs) for explainable recommendation, which leverage disentangled latent preference factors and guide the explanation generation with the refined condition of CVAEs via a self-regularization contrastive learning loss. Extensive experiments demonstrate that our method generates high-quality explanations and achieves new state-of-the-art results in diverse domains.

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