Multi-modal Event Argument Extraction task (MEAE) aims to extract all arguments related to a specific event from multiple modalities and identify their corresponding roles. Existing methods focus on weakly alignment of uni-modal representations and generatively data augmentation techniques. However, these methods ignore the potential impact of event role information on MEAE. To address this problem, we propose a Cross-modal Variational Role Hypergraph Network via Semantic Enhancement (CVRH). Unlike previous approaches, CVRH centers on event role information and designs a variational role hyperedge via semantic enhancement, which constructs a role hypergraph for event arguments within multi-modal documents. It explicitly modeling the high-order role correlations among cross-modal arguments in a document. Furthermore, CVRH introduces a modal shared encoder based on differential transformer, which effectively learns shared semantic representations across modalities and enhances the independence of argument representations. On the M2E2 benchmark, experimental results show that CVRH achieves a 6.9% improvement in F1-score on the MEAE compared to current state-of-the-art methods.

Most existing event causality identification (ECI) methods rarely consider the event causal label information and the interaction information between event pairs. In this paper, we propose a framework to enrich the representation of event pairs by introducing the event causal label information and the event pair interaction information. In particular, 1) we design an event-causal-label-aware module to model the event causal label information, in which we design the event causal label prediction task as an auxiliary task of ECI, aiming to predict which events are involved in the causal relationship (we call them causality-related events) by mining the dependencies between events. 2) We further design an event pair interaction graph module to model the interaction information between event pairs, in which we construct the interaction graph with event pairs as nodes and leverage graph attention mechanism to model the degree of dependency between event pairs. The experimental results show that our approach outperforms previous state-of-the-art methods on two benchmark datasets EventStoryLine and Causal-TimeBank.