  Previously, non-autoregressive models were widely perceived as being superior
in generation efficiency but inferior in generation quality due to the
difficulties of modeling multiple target modalities. To enhance the
multi-modality modeling ability, we propose the diffusion glancing transformer,
which employs a modality diffusion process and residual glancing sampling. The
modality diffusion process is a discrete process that interpolates the
multi-modal distribution along the decoding steps, and the residual glancing
sampling approach guides the model to continuously learn the remaining
modalities across the layers. Experimental results on various machine
translation and text generation benchmarks demonstrate that DIFFGLAT achieves
better generation accuracy while maintaining fast decoding speed compared with
both autoregressive and non-autoregressive models.
