HCL-TAT: A Hybrid Contrastive Learning Method for Few-shot Event Detection with Task-Adaptive Threshold
Ruihan Zhang, Wei Wei, Xian-Ling Mao, Rui Fang, Dangyang Chen
Abstract
Event detection has been suffering from constantly emerging event types with lack of sufficient data. Existing works formulate the new problem as few-shot event detection (FSED), and employ two-stage or unified models based on meta-learning to address the problem. However, these methods fall far short of expectations due to: (i) insufficient learning of discriminative representations in low-resource scenarios, and (ii) representation overlap between triggers and non-triggers. To resolve the above issues, in this paper, we propose a novel Hybrid Contrastive Learning method with a Task-Adaptive Threshold (abbreviated as HCL-TAT), which enables discriminative representation learning with a two-view contrastive loss (support-support and prototype-query), and devises an easily-adapted threshold to alleviate misidentification of triggers. Extensive experiments on the benchmark dataset FewEvent demonstrate the superiority of our method to achieve better results compared to the state-of-the-arts. All the data and codes will be available to facilitate future research.- Anthology ID:
- 2022.findings-emnlp.130
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2022
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1808–1819
- Language:
- URL:
- https://aclanthology.org/2022.findings-emnlp.130
- DOI:
- 10.18653/v1/2022.findings-emnlp.130
- Cite (ACL):
- Ruihan Zhang, Wei Wei, Xian-Ling Mao, Rui Fang, and Dangyang Chen. 2022. HCL-TAT: A Hybrid Contrastive Learning Method for Few-shot Event Detection with Task-Adaptive Threshold. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 1808–1819, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- HCL-TAT: A Hybrid Contrastive Learning Method for Few-shot Event Detection with Task-Adaptive Threshold (Zhang et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2022.findings-emnlp.130.pdf