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%0 Conference Proceedings
%T MiniLMv2: Multi-Head Self-Attention Relation Distillation for Compressing Pretrained Transformers
%A Wang, Wenhui
%A Bao, Hangbo
%A Huang, Shaohan
%A Dong, Li
%A Wei, Furu
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F wang-etal-2021-minilmv2
%R 10.18653/v1/2021.findings-acl.188
%U https://aclanthology.org/2021.findings-acl.188
%U https://doi.org/10.18653/v1/2021.findings-acl.188
%P 2140-2151

Markdown (Informal)

[MiniLMv2: Multi-Head Self-Attention Relation Distillation for Compressing Pretrained Transformers](https://aclanthology.org/2021.findings-acl.188) (Wang et al., Findings 2021)

• MiniLMv2: Multi-Head Self-Attention Relation Distillation for Compressing Pretrained Transformers (Wang et al., Findings 2021)

ACL

• Wenhui Wang, Hangbo Bao, Shaohan Huang, Li Dong, and Furu Wei. 2021. MiniLMv2: Multi-Head Self-Attention Relation Distillation for Compressing Pretrained Transformers. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 2140–2151, Online. Association for Computational Linguistics.