Dynamic Prefix-Tuning for Generative Template-based Event Extraction

• URL: http://arxiv.org/abs/2205.06166v1
• Date: Thu, 12 May 2022 15:38:34 GMT
• Title: Dynamic Prefix-Tuning for Generative Template-based Event Extraction
• Authors: Xiao Liu, Heyan Huang, Ge Shi, Bo Wang
• Abstract summary: We propose a generative template-based event extraction method with dynamic prefix (GTEE-DynPref) Experimental results show that our model achieves competitive results with the state-of-the-art classification-based model OneIE on ACE 2005. Our model is proven to be portable to new types of events effectively.
• Score: 31.581360683375337
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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