Event Extraction With Dynamic Prefix Tuning and Relevance Retrieval

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 several significant challenges, including using suboptimal prompts, static event type information, and the overwhelming number of irrelevant event types. In this article, we propose a generative template-based method with dynamic prefixes and a relevance retrieval framework for event extraction (GREE) by first integrating context information with type-specific prefixes to learn a context-specific prefix for each context, and then retrieving the relevant event types with an adaptive threshold. 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.