IBMEA: Exploring Variational Information Bottleneck for Multi-modal Entity Alignment
<|reference_start|>IBMEA: Exploring Variational Information Bottleneck for Multi-modal Entity Alignment: Multi-modal entity alignment (MMEA) aims to identify equivalent entities between multi-modal knowledge graphs (MMKGs), where the entities can be associated with related images. Most existing studies integrate multi-modal information heavily relying on the automatically-learned fusion module, rarely suppressing the redundant information for MMEA explicitly. To this end, we explore variational information bottleneck for multi-modal entity alignment (IBMEA), which emphasizes the alignment-relevant information and suppresses the alignment-irrelevant information in generating entity representations. Specifically, we devise multi-modal variational encoders to generate modal-specific entity representations as probability distributions. Then, we propose four modal-specific information bottleneck regularizers, limiting the misleading clues in refining modal-specific entity representations. Finally, we propose a modal-hybrid information contrastive regularizer to integrate all the refined modal-specific representations, enhancing the entity similarity between MMKGs to achieve MMEA. We conduct extensive experiments on two cross-KG and three bilingual MMEA datasets. Experimental results demonstrate that our model consistently outperforms previous state-of-the-art methods, and also shows promising and robust performance in low-resource and high-noise data scenarios.<|reference_end|>
@article{su2024ibmea:,
    title={IBMEA: Exploring Variational Information Bottleneck for Multi-modal
  Entity Alignment},
    author={Taoyu Su, Jiawei Sheng, Shicheng Wang, Xinghua Zhang, Hongbo Xu,
  Tingwen Liu},
    journal={arXiv preprint arXiv:2407.19302},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.19302},
    primaryClass={cs.CL cs.MM}
}