In real-life conversations, the content is diverse, and there exists the
one-to-many problem that requires diverse generation. Previous studies
attempted to introduce discrete or Gaussian-based continuous latent variables
to address the one-to-many problem, but the diversity is limited. Recently,
diffusion models have made breakthroughs in computer vision, and some attempts
have been made in natural language processing. In this paper, we propose
DiffusionDialog, a novel approach to enhance the diversity of dialogue
generation with the help of diffusion model. In our approach, we introduce
continuous latent variables into the diffusion model. The problem of using
latent variables in the dialog task is how to build both an effective prior of
the latent space and an inferring process to obtain the proper latent given the
context. By combining the encoder and latent-based diffusion model, we encode
the response's latent representation in a continuous space as the prior,
instead of fixed Gaussian distribution or simply discrete ones. We then infer
the latent by denoising step by step with the diffusion model. The experimental
results show that our model greatly enhances the diversity of dialog responses
while maintaining coherence. Furthermore, in further analysis, we find that our
diffusion model achieves high inference efficiency, which is the main challenge
of applying diffusion models in natural language processing.

DiffusionDialog 是一种新的方法，通过引入扩散模型来增强对话生成的多样性。在该方法中，我们将连续潜变量引入扩散模型，通过与编码器结合，将响应的潜在表示编码为连续空间的先验，并通过扩散模型逐步去噪来推断潜变量。实验结果表明，我们的模型在保持连贯性的同时极大地增强了对话回应的多样性。此外，进一步的分析发现我们的扩散模型实现了高效的推断，这是在自然语言处理中应用扩散模型的主要挑战。