BAM! Born-Again Multi-Task Networks for Natural Language Understanding
Kevin Clark, Minh-Thang Luong, Urvashi Khandelwal, Christopher D. Manning, Quoc V. Le
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.- Anthology ID:
- P19-1595
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5931–5937
- Language:
- URL:
- https://aclanthology.org/P19-1595
- DOI:
- 10.18653/v1/P19-1595
- Cite (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.
- Cite (Informal):
- BAM! Born-Again Multi-Task Networks for Natural Language Understanding (Clark et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/P19-1595.pdf
- Code
- google-research/google-research
- Data
- CoLA, GLUE, MRPC, MultiNLI, SST