Multi-Grained Query-Guided Set Prediction Network for Grounded Multimodal Named Entity Recognition

TL;DR

This paper introduces MQSPN, a novel set prediction network for grounded multimodal named entity recognition, effectively modeling intra- and inter-entity relationships to improve extraction accuracy.

Contribution

The paper proposes a unified framework with learnable queries and a fusion network to better model relationships in GMNER, surpassing previous methods.

Findings

Achieves state-of-the-art results on benchmark datasets.

Effectively models intra- and inter-entity relationships.

Outperforms existing unified approaches.

Abstract

Grounded Multimodal Named Entity Recognition (GMNER) is an emerging information extraction (IE) task, aiming to simultaneously extract entity spans, types, and corresponding visual regions of entities from given sentence-image pairs data. Recent unified methods employing machine reading comprehension or sequence generation-based frameworks show limitations in this difficult task. The former, utilizing human-designed type queries, struggles to differentiate ambiguous entities, such as Jordan (Person) and off-White x Jordan (Shoes). The latter, following the one-by-one decoding order, suffers from exposure bias issues. We maintain that these works misunderstand the relationships of multimodal entities. To tackle these, we propose a novel unified framework named Multi-grained Query-guided Set Prediction Network (MQSPN) to learn appropriate relationships at intra-entity and inter-entity levels. Specifically, MQSPN explicitly aligns textual entities with visual regions by employing a set of learnable queries to strengthen intra-entity connections. Based on distinct intra-entity modeling, MQSPN reformulates GMNER as a set prediction, guiding models to establish appropriate inter-entity relationships from a optimal global matching perspective. Additionally, we incorporate a query-guided Fusion Net (QFNet) as a glue network to boost better alignment of two-level relationships. Extensive experiments demonstrate that our approach achieves state-of-the-art performances in widely used benchmarks.