Knowledge interaction graph guided prompting for event causality identification

Event causality identification (ECI) aims to identify causality between event pairs in a text, and is commonly approached as a supervised classification task using pre-trained language models (PLMs). However, limitations in implicit causality identification and insufficient event-knowledge interaction pose significant challenges to ECI. To address these issues, we propose a novel Knowledge Interaction Graph guided Prompt Tuning (KIGP), which leverages prompt tuning and knowledge interaction to fully exploit the potential of PLMs for ECI by integrating external knowledge. Specifically, to accurately capture implicit causality, we design the guidance mechanism and construct event-knowledge interaction graphs that enable external knowledge to enhance event representations through deep interaction between events and knowledge. Experimental results on two benchmark datasets demonstrate that our model outperforms existing approaches significantly.