Dynamic Prefix-Tuning for Generative Template-based Event Extraction
This work addresses event extraction for natural language processing by improving generative methods, though it is incremental as it builds on existing template-based approaches.
The paper tackles event extraction by proposing a generative template-based method with dynamic prefixes to address suboptimal prompts and static event type information, achieving competitive results with the state-of-the-art classification-based model on ACE 2005 and best performances on ERE.
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.