Generative Event Extraction via Internal Knowledge-Enhanced Prompt Learning

Event extraction is a crucial research task in information extraction. In order to maximize the performances of the pre-trained language model (PLM), some works formulating event extraction as a conditional generation problem. However, most existing generative methods ignore the prior information between event entities, and are usually over-dependent on hand-crafted designed templates, which causing subjective intervention. In this paper, we propose a generative event extraction model named KEPGEE based on internal knowledge-enhanced prompt learning. We firstly use relational graph neural networks (RGCN) to encode the event triples entities and fuse them with the word embeddings to obtain the knowledge representation. Then the knowledge representation is concatenated with task-specific virtual tokens to compose knowledge-enhanced soft prompts, which can provide additional event information to adapt the sequence-to-sequence PLM for the generative event extraction task. Besides, in template design, we add the related topic words into the prompt templates to enhance the implicit event information. We evaluate our model on ACE2005 and ERE datasets, and the results show that our model achieves matched or better performances with several classification-based or generation-based event extraction models (including the state-of-the-art models).