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.
@article{arxiv.2205.06166,
title = {Dynamic Prefix-Tuning for Generative Template-based Event Extraction},
author = {Xiao Liu and Heyan Huang and Ge Shi and Bo Wang},
journal= {arXiv preprint arXiv:2205.06166},
year = {2022}
}