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 |
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页 | 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 2022 → 11 12月 2022 |
会议
会议 | 2022 Findings of the Association for Computational Linguistics: EMNLP 2022 |
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国家/地区 | 阿拉伯联合酋长国 |
市 | Abu Dhabi |
时期 | 7/12/22 → 11/12/22 |