Text Diffusion with Reinforced Conditioning
Yuxuan Liu Tianchi Yang Shaohan Huang Zihan Zhang Haizhen Huang Furu Wei Weiwei Deng Feng Sun Qi Zhang
Diffusion models have demonstrated exceptional capability in generating
high-quality images, videos, and audio. Due to their adaptiveness in iterative
refinement, they provide a strong potential for achieving better
non-autoregressive sequence generation. However, existing text diffusion models
still fall short in their performance due to a challenge in handling the
discreteness of language. This paper thoroughly analyzes text diffusion models
and uncovers two significant limitations: degradation of self-conditioning
during training and misalignment between training and sampling. Motivated by
our findings, we propose a novel Text Diffusion model called TREC, which
mitigates the degradation with Reinforced Conditioning and the misalignment by
Time-Aware Variance Scaling. Our extensive experiments demonstrate the
competitiveness of TREC against autoregressive, non-autoregressive, and
diffusion baselines. Moreover, qualitative analysis shows its advanced ability
to fully utilize the diffusion process in refining samples.