| Challenge: | Existing models with fully-connected attention connections are heavy and require large training data. |
| Approach: | They propose a lightweight alternative to the Transformer by sparsifying the fully-connected structure with a star-shaped topology. |
| Outcome: | The proposed model achieves significant performance improvements on 22 datasets on four tasks. |
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Prakhar Ganesh, Yao Chen, Xin Lou, Mohammad Ali Khan, Yin Yang, Hassan Sajjad, Preslav Nakov, Deming Chen, Marianne Winslett
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DoT: An efficient Double Transformer for NLP tasks with tables (2021.findings-acl)
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Easy and Efficient Transformer: Scalable Inference Solution For Large NLP Model (2022.naacl-industry)
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Gongzheng Li, Yadong Xi, Jingzhen Ding, Duan Wang, Ziyang Luo, Rongsheng Zhang, Bai Liu, Changjie Fan, Xiaoxi Mao, Zeng Zhao
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