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.

Document-level Event Extraction (DEE) is a vital task in NLP as it seeks to automatically recognize and extract event information from a document. However, current approaches often overlook intricate relationships among events and subtle correlations among arguments within a document, which can significantly impact the effectiveness of event type recognition and the extraction of cross-sentence arguments in DEE task. This paper proposes a novel Correlation Association Interactive Network (CAINet), comprising two key components: event relationship graph and argument correlation graph. In particular, the event relationship graph models the relationship among various events through structural associations among event nodes and sentence nodes, to improve the accuracy of event recognition. On the other hand, the arguments correlation graph models the correlations among arguments by quantifying the strength of association among arguments, to effectively aggregate cross-sentence arguments, contributing to the overall success of DEE. Furthermore, we use the large language model to execute DEE task experiments. Experimental results show the proposed CAINet outperforms existing state-of-the-art models and large language models in terms of F1-score across two benchmark datasets.