30 citations · 78 across the 8 of their papers we have counts for
8 papers
Diffusion Language Models Can Perform Many Tasks with Scaling and Instruction-Finetuning
Jiasheng Ye, Zaixiang Zheng, Yu Bao +2
The recent surge of generative AI has been fueled by the generative power of diffusion probabilistic models and the scalable capabilities of large language models. Despite their po…
Selective Knowledge Distillation for Non-Autoregressive Neural Machine Translation
Min Liu, Yu Bao, Chengqi Zhao +1
Benefiting from the sequence-level knowledge distillation, the Non-Autoregressive Transformer (NAT) achieves great success in neural machine translation tasks. However, existing kn…
DINOISER: Diffused Conditional Sequence Learning by Manipulating Noises
Jiasheng Ye, Zaixiang Zheng, Yu Bao +2
While diffusion models have achieved great success in generating continuous signals such as images and audio, it remains elusive for diffusion models in learning discrete sequence…
-GLAT: Glancing at Latent Variables for Parallel Text Generation
Yu Bao, Hao Zhou, Shujian Huang +5
Recently, parallel text generation has received widespread attention due to its success in generation efficiency. Although many advanced techniques are proposed to improve its gene…
Non-Autoregressive Translation by Learning Target Categorical Codes
Yu Bao, Shujian Huang, Tong Xiao +3
Non-autoregressive Transformer is a promising text generation model. However, current non-autoregressive models still fall behind their autoregressive counterparts in translation q…
Glancing Transformer for Non-Autoregressive Neural Machine Translation
Lihua Qian, Hao Zhou, Yu Bao +5
Recent work on non-autoregressive neural machine translation (NAT) aims at improving the efficiency by parallel decoding without sacrificing the quality. However, existing NAT meth…