no code implementations • NAACL (AutoSimTrans) 2021 • Shuangtao Li, Jinming Hu, Boli Wang, Xiaodong Shi, Yidong Chen
This paper describes our two systems submitted to the simultaneous translation evaluation at the 2nd automatic simultaneous translation workshop.
no code implementations • 20 Jul 2023 • Yafang Zheng, Lei Lin, Shuangtao Li, Yuxuan Yuan, Zhaohong Lai, Shan Liu, Biao Fu, Yidong Chen, Xiaodong Shi
Inspired by this, we propose LRF, a novel \textbf{L}ayer-wise \textbf{R}epresentation \textbf{F}usion framework for CG, which learns to fuse previous layers' information back into the encoding and decoding process effectively through introducing a \emph{fuse-attention module} at each encoder and decoder layer.
no code implementations • 20 May 2023 • Lei Lin, Shuangtao Li, Yafang Zheng, Biao Fu, Shan Liu, Yidong Chen, Xiaodong Shi
There is mounting evidence that one of the reasons hindering CG is the representation of the encoder uppermost layer is entangled, i. e., the syntactic and semantic representations of sequences are entangled.
no code implementations • 21 Mar 2023 • Lei Lin, Shuangtao Li, Xiaodong Shi
Simultaneous machine translation, which aims at a real-time translation, is useful in many live scenarios but very challenging due to the trade-off between accuracy and latency.
no code implementations • 20 Apr 2018 • Shuangtao Li, Yuanke Chen, Yanlin Peng, Lin Bai
We show that the features learned by neural networks are not robust, and find that the robustness of the learned features is closely related to the resistance against adversarial examples of neural networks.