  This work studies discrete diffusion probabilistic models with applications
to natural language generation. We derive an alternative yet equivalent
formulation of the sampling from discrete diffusion processes and leverage this
insight to develop a family of reparameterized discrete diffusion models. The
derived generic framework is highly flexible, offers a fresh perspective of the
generation process in discrete diffusion models, and features more effective
training and decoding techniques. We conduct extensive experiments to evaluate
the text generation capability of our model, demonstrating significant
improvements over existing diffusion models.
