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Multimodal named entity recognition (MNER) and multimodal relation extraction(MRE) are two fundamental subtasks in the multimodal knowledge graphconstruction task. However, the existing methods usually handle two tasksindependently, which ignores the bidirectional interaction between them. Thispaper is the first to propose jointly performing MNER and MRE as a jointmultimodal entity-relation extraction task (JMERE). Besides, the current MNERand MRE models only consider aligning the visual objects with textual entitiesin visual and textual graphs but ignore the entity-entity relationships andobject-object relationships. To address the above challenges, we propose anedge-enhanced graph alignment network and a word-pair relation tagging (EEGA)for JMERE task. Specifically, we first design a word-pair relation tagging toexploit the bidirectional interaction between MNER and MRE and avoid the errorpropagation. Then, we propose an edge-enhanced graph alignment network toenhance the JMERE task by aligning nodes and edges in the cross-graph. Comparedwith previous methods, the proposed method can leverage the edge information toauxiliary alignment between objects and entities and find the correlationsbetween entity-entity relationships and object-object relationships.Experiments are conducted to show the effectiveness of our model.

Joint Multimodal Entity-Relation Extraction Based on Edge-enhanced Graph Alignment Network and Word-pair Relation Tagging