BAM! Born-Again Multi-Task Networks for Natural Language Understanding

2019-07-10 · ACL 2019 7 · Kevin Clark, Minh-Thang Luong, Urvashi Khandelwal, Christopher D. Manning, Quoc V. Le

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.

📄 PDF Abstract BibTeX arXiv:1907.04829

Code (1)

google-research/google-research/tree/master/bam 공식 구현 jax

Tasks

Knowledge Distillation Natural Language Understanding

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2019-09-01 · WS (NoDaLiDa) 2019 9 · Leon Derczynski, Alex Speed Kjeldsen

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