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%0 Conference Proceedings
%T Adapt-and-Distill: Developing Small, Fast and Effective Pretrained Language Models for Domains
%A Yao, Yunzhi
%A Huang, Shaohan
%A Wang, Wenhui
%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 yao-etal-2021-adapt
%R 10.18653/v1/2021.findings-acl.40
%U https://aclanthology.org/2021.findings-acl.40
%U https://doi.org/10.18653/v1/2021.findings-acl.40
%P 460-470

Markdown (Informal)

[Adapt-and-Distill: Developing Small, Fast and Effective Pretrained Language Models for Domains](https://aclanthology.org/2021.findings-acl.40) (Yao et al., Findings 2021)

• Adapt-and-Distill: Developing Small, Fast and Effective Pretrained Language Models for Domains (Yao et al., Findings 2021)

ACL

• Yunzhi Yao, Shaohan Huang, Wenhui Wang, Li Dong, and Furu Wei. 2021. Adapt-and-Distill: Developing Small, Fast and Effective Pretrained Language Models for Domains. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 460–470, Online. Association for Computational Linguistics.