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.
BibTeX
@inproceedings{liu-etal-2022-dynamic,
title = "Dynamic Prefix-Tuning for Generative Template-based Event Extraction",
author = "Liu, Xiao and
Huang, Heyan and
Shi, Ge and
Wang, Bo",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.358/",
doi = "10.18653/v1/2022.acl-long.358",
pages = "5216--5228"
}