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Extracting relational facts from multimodal data is a crucial task in thefield of multimedia and knowledge graphs that feeds into widespread real-worldapplications. The emphasis of recent studies centers on recognizing relationalfacts in which both entities are present in one modality and supplementaryinformation is used from other modalities. However, such works disregard asubstantial amount of multimodal relational facts that arise across differentmodalities, such as one entity seen in a text and another in an image. In thispaper, we propose a new task, namely Multimodal Object-Entity RelationExtraction, which aims to extract "object-entity" relational facts from imageand text data. To facilitate research on this task, we introduce MORE, a newdataset comprising 21 relation types and 20,264 multimodal relational factsannotated on 3,559 pairs of textual news titles and corresponding images. Toshow the challenges of Multimodal Object-Entity Relation Extraction, weevaluated recent state-of-the-art methods for multimodal relation extractionand conducted a comprehensive experimentation analysis on MORE. Our resultsdemonstrate significant challenges for existing methods, underlining the needfor further research on this task. Based on our experiments, we identifyseveral promising directions for future research. The MORE dataset and code areavailable at https://github.com/NJUNLP/MORE.

MORE: A Multimodal Object-Entity Relation Extraction Dataset with a Benchmark Evaluation