Which *BERT? A Survey Organizing Contextualized Encoders

Patrick Xia, Shijie Wu, Benjamin Van Durme


Abstract
Pretrained contextualized text encoders are now a staple of the NLP community. We present a survey on language representation learning with the aim of consolidating a series of shared lessons learned across a variety of recent efforts. While significant advancements continue at a rapid pace, we find that enough has now been discovered, in different directions, that we can begin to organize advances according to common themes. Through this organization, we highlight important considerations when interpreting recent contributions and choosing which model to use.
Anthology ID:
2020.emnlp-main.608
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7516–7533
Language:
URL:
https://aclanthology.org/2020.emnlp-main.608
DOI:
10.18653/v1/2020.emnlp-main.608
Bibkey:
Cite (ACL):
Patrick Xia, Shijie Wu, and Benjamin Van Durme. 2020. Which *BERT? A Survey Organizing Contextualized Encoders. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7516–7533, Online. Association for Computational Linguistics.
Cite (Informal):
Which *BERT? A Survey Organizing Contextualized Encoders (Xia et al., EMNLP 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/nodalida-main-page/2020.emnlp-main.608.pdf
Video:
 https://slideslive.com/38939146
Data
GLUESQuADSuperGLUE