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

TL;DR

This paper introduces a novel joint multimodal entity-relation extraction model that leverages edge-enhanced graph alignment and word-pair relation tagging to improve the interaction and accuracy in multimodal knowledge graph construction.

Contribution

It is the first to jointly perform MNER and MRE with an edge-enhanced graph alignment network and word-pair relation tagging, capturing richer entity and relation interactions.

Findings

The proposed model outperforms previous methods in accuracy.

Edge information improves node and edge alignment.

Joint modeling reduces error propagation.

Abstract

Multimodal named entity recognition (MNER) and multimodal relation extraction (MRE) are two fundamental subtasks in the multimodal knowledge graph construction task. However, the existing methods usually handle two tasks independently, which ignores the bidirectional interaction between them. This paper is the first to propose jointly performing MNER and MRE as a joint multimodal entity-relation extraction task (JMERE). Besides, the current MNER and MRE models only consider aligning the visual objects with textual entities in visual and textual graphs but ignore the entity-entity relationships and object-object relationships. To address the above challenges, we propose an edge-enhanced graph alignment network and a word-pair relation tagging (EEGA) for JMERE task. Specifically, we first design a word-pair relation tagging to exploit the bidirectional interaction between MNER and MRE and avoid the error propagation. Then, we propose an edge-enhanced graph alignment network to enhance the JMERE task by aligning nodes and edges in the cross-graph. Compared with previous methods, the proposed method can leverage the edge information to auxiliary alignment between objects and entities and find the correlations between entity-entity relationships and object-object relationships. Experiments are conducted to show the effectiveness of our model.