BAM! Born-again Multi-task Networks For Natural Language Understanding

Kevin Clark, Minh-Thang Luong, Urvashi Khandelwal, Christopher D. Manning, Quoc V. Le . Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics 2019 – 222 citations

[Paper]    
ACL Compositional Generalization Efficiency Evaluation Fine Tuning Interdisciplinary Approaches Model Architecture Neural Machine Translation Training Techniques Variational Autoencoders

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.

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