Knowledge-Enhanced Self-Supervised Prototypical Network for Few-Shot Event Detection

Kailin Zhao, Xiaolong Jin, Long Bai, Jiafeng Guo, Xueqi Cheng


Abstract
Prototypical network based joint methods have attracted much attention in few-shot event detection, which carry out event detection in a unified sequence tagging framework. However, these methods suffer from the inaccurate prototype representation problem, due to two main reasons: the number of instances for calculating prototypes is limited; And, they do not well capture the relationships among event prototypes. To deal with this problem, we propose a Knowledge-Enhanced self-supervised Prototypical Network, called KE-PN, for few-shot event detection. KE-PN adopts hybrid rules, which can automatically align event types to an external knowledge base, i.e., FrameNet, to obtain more instances. It proposes a self-supervised learning method to filter out noisy data from enhanced instances. KE-PN is further equipped with an auxiliary event type relationship classification module, which injects the relationship information into representations of event prototypes. Extensive experiments on three benchmark datasets, i.e., FewEvent, MAVEN, and ACE2005 demonstrate the state-of-the-art performance of KE-PN.
Anthology ID:
2022.findings-emnlp.467
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:
6266–6275
Language:
URL:
https://aclanthology.org/2022.findings-emnlp.467
DOI:
10.18653/v1/2022.findings-emnlp.467
Bibkey:
Cite (ACL):
Kailin Zhao, Xiaolong Jin, Long Bai, Jiafeng Guo, and Xueqi Cheng. 2022. Knowledge-Enhanced Self-Supervised Prototypical Network for Few-Shot Event Detection. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 6266–6275, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Knowledge-Enhanced Self-Supervised Prototypical Network for Few-Shot Event Detection (Zhao et al., Findings 2022)
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PDF:
https://aclanthology.org/2022.findings-emnlp.467.pdf