  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 an improved text 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 work of text diffusion, 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. We
present our collection of text diffusion models at
https://github.com/RUCAIBox/Awesome-Text-Diffusion-Models.
