@inproceedings{zou-etal-2026-dear,
    title = "{DEAR}: Distributional Error-Aware Reliability for Robust Multimodal Sentiment Analysis with Missing Modalities",
    author = "Zou, Shihao  and
      Wei, Wei  and
      Zhang, Yongshuo",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1517/",
    pages = "30344--30361",
    ISBN = "979-8-89176-395-1",
    abstract = "Multimodal Sentiment Analysis (MSA) often suffers from performance degradation due to missing modalities in practical applications. Existing methods typically focus on feature completion but neglect semantic shifts caused by distribution gaps and decision risks under high uncertainty. In this paper, we propose a Distributional Error-Aware Reliability (DEAR) estimation framework for robust MSA. Specifically, we design a Hierarchical Distribution-Constrained Reconstruction (HDCR) module to mitigate semantic shifts by explicitly aligning reconstructed features with the original distributional manifold. Meanwhile, a reliability evaluation module (SURE) is introduced to quantitatively measure reconstruction fidelity. By perceiving inherent uncertainty, SURE provides a reliability-driven gating mechanism for the Synergistic-Robust Dual-Stream (SR-DS) architecture. This mechanism enables the model to dynamically adjust contribution weights: strengthening cross-modal synergistic effects when data fidelity is high, while shifting focus toward robust paths under high-risk missingness to safeguard performance. Extensive experiments on MOSI, MOSEI, and SIMS datasets validate the effectiveness and decision reliability of DEAR."
}