Integrating Large Pre-trained Models into Multimodal Named Entity
  Recognition with Evidential Fusion
Weide Liu Xiaoyang Zhong Jingwen Hou Shaohua Li Haozhe Huang Yuming Fang
Multimodal Named Entity Recognition (MNER) is a crucial task for information
extraction from social media platforms such as Twitter. Most current methods
rely on attention weights to extract information from both text and images but
are often unreliable and lack interpretability. To address this problem, we
propose incorporating uncertainty estimation into the MNER task, producing
trustworthy predictions. Our proposed algorithm models the distribution of each
modality as a Normal-inverse Gamma distribution, and fuses them into a unified
distribution with an evidential fusion mechanism, enabling hierarchical
characterization of uncertainties and promotion of prediction accuracy and
trustworthiness. Additionally, we explore the potential of pre-trained large
foundation models in MNER and propose an efficient fusion approach that
leverages their robust feature representations. Experiments on two datasets
demonstrate that our proposed method outperforms the baselines and achieves new
state-of-the-art performance.