Non-autoregressive (NAR) text generation has attracted much attention in the
field of natural language processing, which greatly reduces the inference
latency but has to sacrifice the generation accuracy. Recently, diffusion
models, a class of latent variable generative models, have been introduced into
NAR text generation, showing improved generation quality. In this survey, we
review the recent progress in diffusion models for NAR text generation. As the
background, we first present the general definition of diffusion models and the
text diffusion models, and then discuss their merits for NAR generation. As the
core content, we further introduce two mainstream diffusion models in existing
text diffusion works, and review the key designs of the diffusion process.
Moreover, we discuss the utilization of pre-trained language models (PLMs) for
text diffusion models and introduce optimization techniques for text data.
Finally, we discuss several promising directions and conclude this paper. Our
survey aims to provide researchers with a systematic reference of related
research on text diffusion models for NAR generation.

本文系统介绍了最新的扩散模型在 NAR（非自回归）文本生成领域中的进展，重点介绍了扩散模型的核心设计、预训练语言模型的应用，以及优化技术等。该综述旨在为研究人员提供一个有关文本扩散模型的系统参考。