Multimodal Entity Linking (MEL) aims to link ambiguous mentions in multimodal
contexts to entities in a multimodal knowledge graph. A pivotal challenge is to
fully leverage multi-element correlations between mentions and entities to
bridge modality gap and enable fine-grained semantic matching. Existing methods
attempt several local correlative mechanisms, relying heavily on the
automatically learned attention weights, which may over-concentrate on partial
correlations. To mitigate this issue, we formulate the correlation assignment
problem as an optimal transport (OT) problem, and propose a novel MEL
framework, namely OT-MEL, with OT-guided correlation assignment. Thereby, we
exploit the correlation between multimodal features to enhance multimodal
fusion, and the correlation between mentions and entities to enhance
fine-grained matching. To accelerate model prediction, we further leverage
knowledge distillation to transfer OT assignment knowledge to attention
mechanism. Experimental results show that our model significantly outperforms
previous state-of-the-art baselines and confirm the effectiveness of the
OT-guided correlation assignment.

Multimodal Entity Linking (MEL) uses optimal transport to exploit multi-element correlations and enhance fusion and matching between multimodal features, outperforming previous baselines.