Event causality identification via graph contrast-based knowledge augmented networks

Abstract

Identifying causality between events is a crucial research task in natural language processing. However, existing methods either ignore background knowledge of events or do not consider interference of knowledge graph noise on event representations. In this paper, we propose a novel Graph Contrast-based Knowledge Augmented Network (GCKAN) for event causality identification task, which integrates comprehensive background knowledge of events from knowledge graphs and alleviates the knowledge graph noise problem. First, a descriptive knowledge augmentation module is proposed to aggregate one-hop neighbor information in knowledge graphs and learn meaningful descriptive knowledge of events. Then, a relational knowledge augmentation module is designed to encode multi-hop path information and learn latent reasoning knowledge between event pairs. In addition, trustworthiness-based and degree-based graph contrastive learning schemas are devised in the two modules respectively, which suppress knowledge graph noise during information aggregation and derive more robust knowledge-aware event representations. Extensive experiments on three public datasets demonstrate the consistent superiority of GCKAN over state-of-the-art knowledge-based techniques. Noise interference experiments and cross-topic adaptation experiments further verify the robustness and generalization of GCKAN.