For sequence generation, both autoregressive models and non-autoregressive
models have been developed in recent years. Autoregressive models can achieve
high generation quality, but the sequential decoding scheme causes slow
decoding speed. Non-autoregressive models accelerate the inference speed with
parallel decoding, while their generation quality still needs to be improved
due to the difficulty of modeling multi-modalities in data. To address the
multi-modality issue, we propose Diff-Glat, a non-autoregressive model featured
with a modality diffusion process and residual glancing training. The modality
diffusion process decomposes the modalities and reduces the modalities to learn
for each transition. And the residual glancing sampling further smooths the
modality learning procedures. Experiments demonstrate that, without using
knowledge distillation data, Diff-Glat can achieve superior performance in both
decoding efficiency and accuracy compared with the autoregressive Transformer.

Diff-Glat 是一种采用模态扩散过程和残差凝视训练的非自回归模型，旨在解决多模态数据建模困难问题，相较于自回归变换器，Diff-Glat 在解码效率和准确性方面均表现出卓越的性能。