Bridging the Unseen Gap: Label-Enhanced Information Bottleneck Distillation for Multimodal Named Entity Recognition

摘要

Multimodal Named Entity Recognition (MNER) integrates visual information to resolve textual ambiguities but struggles with generalizing to unseen entities (out-of-vocabulary, OOV), particularly in social media. To bridge this gap, we leveraging internal label knowledge and visual information and propose a Label-Enhanced Information Bottleneck Distillation (LIBD) framework, which transfers label-aware generalization capabilities via a teacher-student architecture. Our method introduces Dual-level Label Augmentation (DLA), enhancing the teacher model by integrating word-level entity replacement with labels and embedding-level learnable label vectors. This is paired with Information Bottleneck Distillation (IBD), selectively distilling critical knowledge from the teacher while suppressing irrelevant noise. Experiments on benchmark datasets demonstrate that LIBD outperforms state-of-the-art methods, especially in identifying OOV entities.