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

*此作品的通讯作者

科研成果: 会议稿件论文同行评审

11 引用 (Scopus)

摘要

Conventional event detection models under supervised learning settings suffer from the inability of transfer to newly-emerged event types owing to lack of sufficient annotations. A commonly-adapted solution is to follow a identify-then-classify manner, which first identifies the triggers and then converts the classification task via a few-shot learning paradigm. However, these methods still fall far short of expectations due to: (i) insufficient learning of discriminative representations in low-resource scenarios, and (ii) trigger misidentification caused by the overlap of the learned representations of triggers and non-triggers. To address the problems, 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 code and data of this paper are available at https://github.com/CCIIPLab/HCL-TAT.

源语言英语
1808-1819
页数12
出版状态已出版 - 2022
活动2022 Findings of the Association for Computational Linguistics: EMNLP 2022 - Abu Dhabi, 阿拉伯联合酋长国
期限: 7 12月 202211 12月 2022

会议

会议2022 Findings of the Association for Computational Linguistics: EMNLP 2022
国家/地区阿拉伯联合酋长国
Abu Dhabi
时期7/12/2211/12/22

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引用此

Zhang, R., Wei, W., Mao, X. L., Fang, R., & Chen, D. (2022). HCL-TAT: A Hybrid Contrastive Learning Method for Few-shot Event Detection with Task-Adaptive Threshold. 1808-1819. 论文发表于 2022 Findings of the Association for Computational Linguistics: EMNLP 2022, Abu Dhabi, 阿拉伯联合酋长国.