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UniDrop: A Simple yet Effective Technique to Improve Transformer without Extra Cost
[article]
2021
arXiv
pre-print
Preserved Fulltext
With some explorations, we find simple techniques such as dropout, can greatly boost model performance with a careful design. ...
Specifically, we propose an approach named UniDrop to unites three different dropout techniques from fine-grain to coarse-grain, i.e., feature dropout, structure dropout, and data dropout. ...
Acknowledgments The authors would like to thank the anonymous reviewers for their valuable comments. Xinyu Dai and Lijun Wu are the corresponding authors. ...
arXiv:2104.04946v1
fatcat:6dqmk6fzbnh3fmvbee3gq3xkne
UniDrop: A Simple yet Effective Technique to Improve Transformer without Extra Cost
2021
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
unpublished
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
With some explorations, we find simple techniques such as dropout, can greatly boost model performance with a careful design. ...
Specifically, we propose an approach named UniDrop to unite three different dropout techniques from fine-grain to coarse-grain, i.e., feature dropout, structure dropout, and data dropout. ...
Acknowledgments The authors would like to thank the anonymous reviewers for their valuable comments. Xinyu Dai and Lijun Wu are the corresponding authors. ...
doi:10.18653/v1/2021.naacl-main.302
fatcat:oafhegiwrvbz7l5plzslvkihh4