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    <abstract>Multimodal Sentiment Analysis (MSA) with incomplete data has gained significant attention recently. Existing studies focus on optimizing model structures to handle modality missingness, but models still face challenges in robustness when dealing with uncertain missingness. To this end, we propose a data-centric robust multimodal sentiment analysis method, Proxy-Driven Robust Multimodal Fusion (P-RMF). First, we map unimodal data to the latent space of Gaussian distributions to capture core features and structure, thereby learn stable modality representation. Then, we combine the quantified inherent modality uncertainty to learn stable multimodal joint representation (i.e., proxy modality), which is further enhanced through multi-layer dynamic cross-modal injection to increase its diversity. Extensive experimental results show that P-RMF outperforms existing models in noise resistance and achieves state-of-the-art performance on multiple benchmark datasets. Code will be available at https://github.com/***/P-RMF.</abstract>
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%0 Conference Proceedings
%T Proxy-Driven Robust Multimodal Sentiment Analysis with Incomplete Data
%A Zhu, Aoqiang
%A Hu, Min
%A Wang, Xiaohua
%A Yang, Jiaoyun
%A Tang, Yiming
%A An, Ning
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F zhu-etal-2025-proxy
%X Multimodal Sentiment Analysis (MSA) with incomplete data has gained significant attention recently. Existing studies focus on optimizing model structures to handle modality missingness, but models still face challenges in robustness when dealing with uncertain missingness. To this end, we propose a data-centric robust multimodal sentiment analysis method, Proxy-Driven Robust Multimodal Fusion (P-RMF). First, we map unimodal data to the latent space of Gaussian distributions to capture core features and structure, thereby learn stable modality representation. Then, we combine the quantified inherent modality uncertainty to learn stable multimodal joint representation (i.e., proxy modality), which is further enhanced through multi-layer dynamic cross-modal injection to increase its diversity. Extensive experimental results show that P-RMF outperforms existing models in noise resistance and achieves state-of-the-art performance on multiple benchmark datasets. Code will be available at https://github.com/***/P-RMF.
%R 10.18653/v1/2025.acl-long.1075
%U https://aclanthology.org/2025.acl-long.1075/
%U https://doi.org/10.18653/v1/2025.acl-long.1075
%P 22123-22138

Markdown (Informal)

[Proxy-Driven Robust Multimodal Sentiment Analysis with Incomplete Data](https://aclanthology.org/2025.acl-long.1075/) (Zhu et al., ACL 2025)

• Proxy-Driven Robust Multimodal Sentiment Analysis with Incomplete Data (Zhu et al., ACL 2025)

ACL

• Aoqiang Zhu, Min Hu, Xiaohua Wang, Jiaoyun Yang, Yiming Tang, and Ning An. 2025. Proxy-Driven Robust Multimodal Sentiment Analysis with Incomplete Data. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 22123–22138, Vienna, Austria. Association for Computational Linguistics.