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arXiv · 2205.06166

Dynamic Prefix-Tuning for Generative Template-based Event Extraction

Xiao Liu·Heyan Huang·Ge Shi·Bo Wang
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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.

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Xiao Liu, Heyan Huang, Ge Shi, Bo Wang. 2022-05-12. Dynamic Prefix-Tuning for Generative Template-based Event Extraction. https://doi.org/10.18653/v1/2022.acl-long.358

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