BanditMTL: Bandit-based Multi-task Learning for Text Classification

Yuren Mao, Zekai Wang, Weiwei Liu, Xuemin Lin, Wenbin Hu


Abstract
Task variance regularization, which can be used to improve the generalization of Multi-task Learning (MTL) models, remains unexplored in multi-task text classification. Accordingly, to fill this gap, this paper investigates how the task might be effectively regularized, and consequently proposes a multi-task learning method based on adversarial multi-armed bandit. The proposed method, named BanditMTL, regularizes the task variance by means of a mirror gradient ascent-descent algorithm. Adopting BanditMTL in the multi-task text classification context is found to achieve state-of-the-art performance. The results of extensive experiments back up our theoretical analysis and validate the superiority of our proposals.
Anthology ID:
2021.acl-long.428
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5506–5516
Language:
URL:
https://aclanthology.org/2021.acl-long.428
DOI:
10.18653/v1/2021.acl-long.428
Bibkey:
Cite (ACL):
Yuren Mao, Zekai Wang, Weiwei Liu, Xuemin Lin, and Wenbin Hu. 2021. BanditMTL: Bandit-based Multi-task Learning for Text Classification. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 5506–5516, Online. Association for Computational Linguistics.
Cite (Informal):
BanditMTL: Bandit-based Multi-task Learning for Text Classification (Mao et al., ACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-long.428.pdf
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 2021.acl-long.428.OptionalSupplementaryMaterial.zip
Video:
 https://aclanthology.org/2021.acl-long.428.mp4