Dynamic Prefix-Tuning for Generative Template-based Event Extraction

Xiao Liu, Heyan Huang, Ge Shi, Bo Wang


Abstract
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-of-the-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.
Anthology ID:
2022.acl-long.358
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5216–5228
Language:
URL:
https://aclanthology.org/2022.acl-long.358
DOI:
10.18653/v1/2022.acl-long.358
Bibkey:
Cite (ACL):
Xiao Liu, Heyan Huang, Ge Shi, and Bo Wang. 2022. Dynamic Prefix-Tuning for Generative Template-based Event Extraction. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5216–5228, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Dynamic Prefix-Tuning for Generative Template-based Event Extraction (Liu et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.358.pdf