Document-level Event Extraction Method Based on Slot Semantic Enhanced Prompt Learning

Abstract

Event extraction aims to recognize and extract event information from unstructured natural language texts in a structured form.Traditional methods extract events at the sentence level, relying on massive labeled data for training, which are unqualified for document-level event extraction and lack performance in low-resource scenarios.Existing research utilizes prompt learning methods to achieve document-level event extraction by filling in template slots.However, traditional prompt template slots have low accuracy in classifying argument roles, which can easily lead to errors in argument role extraction.To address the above issues, this paper proposes a document-level event extraction method based on slot semantic enhancement prompt learning.Based on the prompt learning method, the argument role semantic information in the traditional event extraction paradigm is integrated into the slot of the prompt template, providing argument type constraints for the slot prediction generation process of the model and improving the accuracy of document-level event extraction.By keeping the upstream and downstream tasks of the pretrained language model consistent, the generalization ability of the model is improved, and knowledge transfer is achieved at a lower cost to improve model performance in low-resource event extraction scenarios.Experimental results show that compared to the traditional baseline method with suboptimal performance, this method achieved an F1 score improvement of 2.6, 2.9, and 4.0 percentage points on an English event extraction dataset containing 59 argument types, Chinese dataset containing 92 argument types, and low-resource data scale, respectively.