Dynamic Prefix-Tuning for Generative Template-based Event Extraction [article]

Xiao Liu, Heyan Huang, Ge Shi, Bo Wang
2022 arXiv   pre-print
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
more » ... th 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.
arXiv:2205.06166v1 fatcat:la5vzkmjofhlld2wdwczlrx2q4