Conditional variational autoencoders (CVAEs) have been used recently for
diverse response generation, by introducing latent variables to represent the
relationship between a dialog context and its potential responses. However, the
diversity of the generated responses brought by a CVAE model is limited due to
the oversimplified assumption of the isotropic Gaussian prior. We propose,
Dior-CVAE, a hierarchical CVAE model with an informative prior produced by a
diffusion model. Dior-CVAE derives a series of layer-wise latent variables
using attention mechanism and infusing them into decoder layers accordingly. We
propose memory dropout in the latent infusion to alleviate posterior collapse.
The prior distribution of the latent variables is parameterized by a diffusion
model to introduce a multimodal distribution. Overall, experiments on two
popular open-domain dialog datasets indicate the advantages of our approach
over previous Transformer-based variational dialog models in dialog response
generation. We publicly release the code for reproducing Dior-CVAE and all
baselines at
this https URL

提出了一种使用扩散模型产生信息量丰富的先验分布的分层条件变分自动编码器（Hierarchical CVAE），并使用注意机制将生成的潜变量逐层注入解码器，使用记忆丢弃解决后验崩溃问题。实验表明，与基于变分的 Transformer 模型相比，该方法在对话生成方面具有优势。