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Dynamic Prefix-Tuning for Generative Template-based Event Extraction
- Publication Year :
- 2022
-
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.<br />Comment: accepted by ACL 2022
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2205.06166
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.18653/v1/2022.acl-long.358