Adapt-and-Distill: Developing Small, Fast and Effective Pretrained Language Models for Domains

Yunzhi Yao, Shaohan Huang, Wenhui Wang, Li Dong, Furu Wei


Anthology ID:
2021.findings-acl.40
Volume:
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
Month:
August
Year:
2021
Address:
Online
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
460–470
Language:
URL:
https://aclanthology.org/2021.findings-acl.40
DOI:
10.18653/v1/2021.findings-acl.40
Bibkey:
Cite (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.
Cite (Informal):
Adapt-and-Distill: Developing Small, Fast and Effective Pretrained Language Models for Domains (Yao et al., Findings 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.findings-acl.40.pdf
Video:
 https://aclanthology.org/2021.findings-acl.40.mp4
Data
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