  Recently, continuous diffusion models (CDM) have been introduced into
non-autoregressive (NAR) text-to-text generation. However, the discrete nature
of text increases the difficulty of CDM to generate coherent and fluent texts,
and also causes the incompatibility problem between CDM and advanced NLP
techniques, especially the popular pre-trained language models~(PLMs). To solve
it, we propose Diffusion-NAT, which introduces discrete diffusion models~(DDM)
into NAR text-to-text generation and integrates BART to improve the
performance. By revising the decoding process of BART and the typical settings
of DDM, we unify the inference process of BART and the denoising process of DDM
into the same NAR masked tokens recovering task. In this way, DDM can rely on
BART to perform denoising, which can benefit from both the rich pre-learned
knowledge of BART and the iterative refining paradigm of DDM. Besides, we also
propose the iterative self-prompting strategy to further improve the generation
quality. Experimental results on 7 datasets show that our approach can
outperform competitive NAR methods, and even surpass autoregressive methods.
Our code and data will be publicly released.
