Abstract
Building general reading comprehension systems, capable of solving multiple datasets at the same time, is a recent aspirational goal in the research community. Prior work has focused on model architecture or generalization to held out datasets, and largely passed over the particulars of the multi-task learning set up. We show that a simple dynamic sampling strategy, selecting instances for training proportional to the multi-task model’s current performance on a dataset relative to its single task performance, gives substantive gains over prior multi-task sampling strategies, mitigating the catastrophic forgetting that is common in multi-task learning. We also demonstrate that allowing instances of different tasks to be interleaved as much as possible between each epoch and batch has a clear benefit in multitask performance over forcing task homogeneity at the epoch or batch level. Our final model shows greatly increased performance over the best model on ORB, a recently-released multitask reading comprehension benchmark.- Anthology ID:
- 2020.acl-main.86
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 920–924
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.86
- DOI:
- 10.18653/v1/2020.acl-main.86
- Cite (ACL):
- Ananth Gottumukkala, Dheeru Dua, Sameer Singh, and Matt Gardner. 2020. Dynamic Sampling Strategies for Multi-Task Reading Comprehension. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 920–924, Online. Association for Computational Linguistics.
- Cite (Informal):
- Dynamic Sampling Strategies for Multi-Task Reading Comprehension (Gottumukkala et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.acl-main.86.pdf