Abstract
We address the sampling bias and outlier issues in few-shot learning for event detection, a subtask of information extraction. We propose to model the relations between training tasks in episodic few-shot learning by introducing cross-task prototypes. We further propose to enforce prediction consistency among classifiers across tasks to make the model more robust to outliers. Our extensive experiment shows a consistent improvement on three few-shot learning datasets. The findings suggest that our model is more robust when labeled data of novel event types is limited. The source code is available at http://github.com/laiviet/fsl-proact.- Anthology ID:
- 2021.emnlp-main.427
- Volume:
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2021
- Address:
- Online and Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5270–5277
- Language:
- URL:
- https://aclanthology.org/2021.emnlp-main.427
- DOI:
- 10.18653/v1/2021.emnlp-main.427
- Cite (ACL):
- Viet Lai, Franck Dernoncourt, and Thien Huu Nguyen. 2021. Learning Prototype Representations Across Few-Shot Tasks for Event Detection. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 5270–5277, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Learning Prototype Representations Across Few-Shot Tasks for Event Detection (Lai et al., EMNLP 2021)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2021.emnlp-main.427.pdf
- Code
- laiviet/fsl-proact