Learning Task Sampling Policy for Multitask Learning
Dhanasekar Sundararaman, Henry Tsai, Kuang-Huei Lee, Iulia Turc, Lawrence Carin
Abstract
It has been shown that training multi-task models with auxiliary tasks can improve the target task quality through cross-task transfer. However, the importance of each auxiliary task to the primary task is likely not known a priori. While the importance weights of auxiliary tasks can be manually tuned, it becomes practically infeasible with the number of tasks scaling up. To address this, we propose a search method that automatically assigns importance weights. We formulate it as a reinforcement learning problem and learn a task sampling schedule based on the evaluation accuracy of the multi-task model. Our empirical evaluation on XNLI and GLUE shows that our method outperforms uniform sampling and the corresponding single-task baseline.- Anthology ID:
- 2021.findings-emnlp.375
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2021
- Month:
- November
- Year:
- 2021
- Address:
- Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- Findings
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4410–4415
- Language:
- URL:
- https://aclanthology.org/2021.findings-emnlp.375
- DOI:
- 10.18653/v1/2021.findings-emnlp.375
- Cite (ACL):
- Dhanasekar Sundararaman, Henry Tsai, Kuang-Huei Lee, Iulia Turc, and Lawrence Carin. 2021. Learning Task Sampling Policy for Multitask Learning. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 4410–4415, Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Learning Task Sampling Policy for Multitask Learning (Sundararaman et al., Findings 2021)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2021.findings-emnlp.375.pdf
- Data
- GLUE, MultiNLI, QNLI