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%0 Conference Proceedings
%T To Asymmetry and Beyond: Structured Pruning of Sequence to Sequence Models for Improved Inference Efficiency
%A Campos, Daniel
%A Zhai, Chengxiang
%Y Sadat Moosavi, Nafise
%Y Gurevych, Iryna
%Y Hou, Yufang
%Y Kim, Gyuwan
%Y Kim, Young Jin
%Y Schuster, Tal
%Y Agrawal, Ameeta
%S Proceedings of The Fourth Workshop on Simple and Efficient Natural Language Processing (SustaiNLP)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada (Hybrid)
%F campos-zhai-2023-asymmetry
%R 10.18653/v1/2023.sustainlp-1.6
%U https://aclanthology.org/2023.sustainlp-1.6
%U https://doi.org/10.18653/v1/2023.sustainlp-1.6
%P 91-109

Markdown (Informal)

[To Asymmetry and Beyond: Structured Pruning of Sequence to Sequence Models for Improved Inference Efficiency](https://aclanthology.org/2023.sustainlp-1.6) (Campos & Zhai, sustainlp 2023)

• To Asymmetry and Beyond: Structured Pruning of Sequence to Sequence Models for Improved Inference Efficiency (Campos & Zhai, sustainlp 2023)

ACL

• Daniel Campos and Chengxiang Zhai. 2023. To Asymmetry and Beyond: Structured Pruning of Sequence to Sequence Models for Improved Inference Efficiency. In Proceedings of The Fourth Workshop on Simple and Efficient Natural Language Processing (SustaiNLP), pages 91–109, Toronto, Canada (Hybrid). Association for Computational Linguistics.