Dynamic Prefix-Tuning for Generative Template-based Event Extraction

Xiao Liu, Heyan Huang*, Ge Shi, Bo Wang

*此作品的通讯作者

科研成果: 书/报告/会议事项章节会议稿件同行评审

67 引用 (Scopus)

摘要

We consider event extraction in a generative manner with template-based conditional generation. Although there is a rising trend of casting the task of event extraction as a sequence generation problem with prompts, these generation-based methods have two significant challenges, including using suboptimal prompts and static event type information. In this paper, we propose a generative template-based event extraction method with dynamic prefix (GTEE-DYNPREF) by integrating context information with type-specific prefixes to learn a context-specific prefix for each context. Experimental results show that our model achieves competitive results with the state-ofthe-art classification-based model ONEIE on ACE 2005 and achieves the best performances on ERE. Additionally, our model is proven to be portable to new types of events effectively.

源语言英语
主期刊名ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)
编辑Smaranda Muresan, Preslav Nakov, Aline Villavicencio
出版商Association for Computational Linguistics (ACL)
5216-5228
页数13
ISBN(电子版)9781955917216
出版状态已出版 - 2022
活动60th Annual Meeting of the Association for Computational Linguistics, ACL 2022 - Dublin, 爱尔兰
期限: 22 5月 202227 5月 2022

出版系列

姓名Proceedings of the Annual Meeting of the Association for Computational Linguistics
1
ISSN(印刷版)0736-587X

会议

会议60th Annual Meeting of the Association for Computational Linguistics, ACL 2022
国家/地区爱尔兰
Dublin
时期22/05/2227/05/22

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