Event causality identification via graph contrast-based knowledge augmented networks

Ling Ding, Jianting Chen, Peng Du, Yang Xiang

Abstract: Highlights•A graph contrast-based knowledge augmented network for event causality identification is proposed.•The model aggregates descriptive and relational knowledge from knowledge graphs and alleviates labeled data scarcity issue.•Graph contrastive learning schemes are devised to suppress knowledge noise and improve robustness of event representations.•Experimental results demonstrate effectiveness and robustness of the method.