no code implementations • ACL 2022 • Dengji Guo, Zhengrui Ma, Min Zhang, Yang Feng
Regularization methods applying input perturbation have drawn considerable attention and have been frequently explored for NMT tasks in recent years.
1 code implementation • 20 Feb 2024 • Shoutao Guo, Shaolei Zhang, Zhengrui Ma, Min Zhang, Yang Feng
We propose SiLLM, which delegates the two sub-tasks to separate agents, thereby incorporating LLM into SiMT.
no code implementations • 14 Nov 2023 • Shangtong Gui, Chenze Shao, Zhengrui Ma, Xishan Zhang, Yunji Chen, Yang Feng
Non-autoregressive Transformer(NAT) significantly accelerates the inference of neural machine translation.
1 code implementation • 23 Oct 2023 • Zhengrui Ma, Shaolei Zhang, Shoutao Guo, Chenze Shao, Min Zhang, Yang Feng
Simultaneous machine translation (SiMT) models are trained to strike a balance between latency and translation quality.
1 code implementation • 19 Jun 2023 • Shaolei Zhang, Qingkai Fang, Zhuocheng Zhang, Zhengrui Ma, Yan Zhou, Langlin Huang, Mengyu Bu, Shangtong Gui, Yunji Chen, Xilin Chen, Yang Feng
To minimize human workload, we propose to transfer the capabilities of language generation and instruction following from English to other languages through an interactive translation task.
1 code implementation • 12 Mar 2023 • Zhengrui Ma, Chenze Shao, Shangtong Gui, Min Zhang, Yang Feng
Non-autoregressive translation (NAT) reduces the decoding latency but suffers from performance degradation due to the multi-modality problem.
1 code implementation • 11 Oct 2022 • Chenze Shao, Zhengrui Ma, Yang Feng
Non-autoregressive models achieve significant decoding speedup in neural machine translation but lack the ability to capture sequential dependency.
1 code implementation • 18 Jun 2021 • Peng Chen, Liang Liu, Zhengrui Ma, Zhao Kang
In recent years, multi-view subspace clustering has achieved impressive performance due to the exploitation of complementary imformation across multiple views.
1 code implementation • 18 Jun 2021 • Zhengrui Ma, Zhao Kang, Guangchun Luo, Ling Tian
The success of subspace clustering depends on the assumption that the data can be separated into different subspaces.