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    <abstract>Pre-trained language models like BERT have proven to be highly performant. However, they are often computationally expensive in many practical scenarios, for such heavy models can hardly be readily implemented with limited resources. To improve their efficiency with an assured model performance, we propose a novel speed-tunable FastBERT with adaptive inference time. The speed at inference can be flexibly adjusted under varying demands, while redundant calculation of samples is avoided. Moreover, this model adopts a unique self-distillation mechanism at fine-tuning, further enabling a greater computational efficacy with minimal loss in performance. Our model achieves promising results in twelve English and Chinese datasets. It is able to speed up by a wide range from 1 to 12 times than BERT if given different speedup thresholds to make a speed-performance tradeoff.</abstract>
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%0 Conference Proceedings
%T FastBERT: a Self-distilling BERT with Adaptive Inference Time
%A Liu, Weijie
%A Zhou, Peng
%A Wang, Zhiruo
%A Zhao, Zhe
%A Deng, Haotang
%A Ju, Qi
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F liu-etal-2020-fastbert
%X Pre-trained language models like BERT have proven to be highly performant. However, they are often computationally expensive in many practical scenarios, for such heavy models can hardly be readily implemented with limited resources. To improve their efficiency with an assured model performance, we propose a novel speed-tunable FastBERT with adaptive inference time. The speed at inference can be flexibly adjusted under varying demands, while redundant calculation of samples is avoided. Moreover, this model adopts a unique self-distillation mechanism at fine-tuning, further enabling a greater computational efficacy with minimal loss in performance. Our model achieves promising results in twelve English and Chinese datasets. It is able to speed up by a wide range from 1 to 12 times than BERT if given different speedup thresholds to make a speed-performance tradeoff.
%R 10.18653/v1/2020.acl-main.537
%U https://aclanthology.org/2020.acl-main.537
%U https://doi.org/10.18653/v1/2020.acl-main.537
%P 6035-6044

Markdown (Informal)

[FastBERT: a Self-distilling BERT with Adaptive Inference Time](https://aclanthology.org/2020.acl-main.537) (Liu et al., ACL 2020)

• FastBERT: a Self-distilling BERT with Adaptive Inference Time (Liu et al., ACL 2020)

ACL

• Weijie Liu, Peng Zhou, Zhiruo Wang, Zhe Zhao, Haotang Deng, and Qi Ju. 2020. FastBERT: a Self-distilling BERT with Adaptive Inference Time. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6035–6044, Online. Association for Computational Linguistics.