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        <title>BAM! Born-Again Multi-Task Networks for Natural Language Understanding</title>
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        <namePart type="given">Kevin</namePart>
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        <namePart type="given">Minh-Thang</namePart>
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    <abstract>It can be challenging to train multi-task neural networks that outperform or even match their single-task counterparts. To help address this, we propose using knowledge distillation where single-task models teach a multi-task model. We enhance this training with teacher annealing, a novel method that gradually transitions the model from distillation to supervised learning, helping the multi-task model surpass its single-task teachers. We evaluate our approach by multi-task fine-tuning BERT on the GLUE benchmark. Our method consistently improves over standard single-task and multi-task training.</abstract>
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%0 Conference Proceedings
%T BAM! Born-Again Multi-Task Networks for Natural Language Understanding
%A Clark, Kevin
%A Luong, Minh-Thang
%A Khandelwal, Urvashi
%A Manning, Christopher D.
%A Le, Quoc V.
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F clark-etal-2019-bam
%X It can be challenging to train multi-task neural networks that outperform or even match their single-task counterparts. To help address this, we propose using knowledge distillation where single-task models teach a multi-task model. We enhance this training with teacher annealing, a novel method that gradually transitions the model from distillation to supervised learning, helping the multi-task model surpass its single-task teachers. We evaluate our approach by multi-task fine-tuning BERT on the GLUE benchmark. Our method consistently improves over standard single-task and multi-task training.
%R 10.18653/v1/P19-1595
%U https://aclanthology.org/P19-1595/
%U https://doi.org/10.18653/v1/P19-1595
%P 5931-5937

Markdown (Informal)

[BAM! Born-Again Multi-Task Networks for Natural Language Understanding](https://aclanthology.org/P19-1595/) (Clark et al., ACL 2019)

• BAM! Born-Again Multi-Task Networks for Natural Language Understanding (Clark et al., ACL 2019)

ACL

• Kevin Clark, Minh-Thang Luong, Urvashi Khandelwal, Christopher D. Manning, and Quoc V. Le. 2019. BAM! Born-Again Multi-Task Networks for Natural Language Understanding. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5931–5937, Florence, Italy. Association for Computational Linguistics.