ProBioRE: A Framework for Biomedical Causal Relation Extraction Based on Dual-head Prompt and Prototypical Network

Lishuang Li, Wanting Ning

Abstract: Extracting relationships among events typically aims to recognize the precise relation between two given events. For the task of event causal relation extraction in the biomedical domain, it has been extremely challenging since the issue of class imbalance. Existing work proposes to address that challenge with certain traditional data augmentation methods, but this approach has been of limited help. Furthermore, existing work lacks fine-grained event relation identification for available datasets, but focuses only on determining whether two given events are causally related or not. With the development of pre-trained language models in recent years, inspired by prompt learning, we propose a framework for biomedical event causal relation extraction based on dual-head prompt augmentation and prototypical network filtering. By comparing the framework with existing methods, we validate the effectiveness of our method.