@inproceedings{he-etal-2026-uncertainty,
title = "Uncertainty-Calibrated Elastic Alignment for Multimodal Sentiment Analysis with Missing Modalities",
author = "He, Kang and
Ding, Yuzhe and
Fu, Rao and
Feng, Yukang and
Zhang, Kaipeng and
Liu, Yiming and
Li, Fei and
Teng, Chong and
Ji, Donghong",
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://aclanthology.org/2026.findings-acl.260/",
pages = "5268--5288",
ISBN = "979-8-89176-395-1",
abstract = "Multimodal sentiment analysis (MSA) in real-world scenarios is often challenged by dynamically missing modalities. Existing methods predominantly rely on deterministic imputation and rigid alignment, which compels the model to overfit noise in ambiguous regions while neglecting the decision shift induced by modality inertia. To address these issues, we propose a novel uncertainty-calibrated elastic alignment framework, termed EASE. Specifically, we employ probabilistic imputation to capture cross-modal ambiguity and leverage the estimated uncertainty to drive elastic alignment, thereby adaptively relaxing constraints in ambiguous regions to avoid rigid fitting. Meanwhile, we introduce cross-view predictive consistency constraints to unify discriminative logic across different modality views, stabilizing the decision boundary under modality degradation. Extensive experiments demonstrate that EASE consistently outperforms existing state-of-the-art baselines across multiple benchmarks, exhibiting exceptional robustness particularly under high missing-rate scenarios."
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<abstract>Multimodal sentiment analysis (MSA) in real-world scenarios is often challenged by dynamically missing modalities. Existing methods predominantly rely on deterministic imputation and rigid alignment, which compels the model to overfit noise in ambiguous regions while neglecting the decision shift induced by modality inertia. To address these issues, we propose a novel uncertainty-calibrated elastic alignment framework, termed EASE. Specifically, we employ probabilistic imputation to capture cross-modal ambiguity and leverage the estimated uncertainty to drive elastic alignment, thereby adaptively relaxing constraints in ambiguous regions to avoid rigid fitting. Meanwhile, we introduce cross-view predictive consistency constraints to unify discriminative logic across different modality views, stabilizing the decision boundary under modality degradation. Extensive experiments demonstrate that EASE consistently outperforms existing state-of-the-art baselines across multiple benchmarks, exhibiting exceptional robustness particularly under high missing-rate scenarios.</abstract>
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%0 Conference Proceedings
%T Uncertainty-Calibrated Elastic Alignment for Multimodal Sentiment Analysis with Missing Modalities
%A He, Kang
%A Ding, Yuzhe
%A Fu, Rao
%A Feng, Yukang
%A Zhang, Kaipeng
%A Liu, Yiming
%A Li, Fei
%A Teng, Chong
%A Ji, Donghong
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F he-etal-2026-uncertainty
%X Multimodal sentiment analysis (MSA) in real-world scenarios is often challenged by dynamically missing modalities. Existing methods predominantly rely on deterministic imputation and rigid alignment, which compels the model to overfit noise in ambiguous regions while neglecting the decision shift induced by modality inertia. To address these issues, we propose a novel uncertainty-calibrated elastic alignment framework, termed EASE. Specifically, we employ probabilistic imputation to capture cross-modal ambiguity and leverage the estimated uncertainty to drive elastic alignment, thereby adaptively relaxing constraints in ambiguous regions to avoid rigid fitting. Meanwhile, we introduce cross-view predictive consistency constraints to unify discriminative logic across different modality views, stabilizing the decision boundary under modality degradation. Extensive experiments demonstrate that EASE consistently outperforms existing state-of-the-art baselines across multiple benchmarks, exhibiting exceptional robustness particularly under high missing-rate scenarios.
%U https://aclanthology.org/2026.findings-acl.260/
%P 5268-5288
Markdown (Informal)
[Uncertainty-Calibrated Elastic Alignment for Multimodal Sentiment Analysis with Missing Modalities](https://aclanthology.org/2026.findings-acl.260/) (He et al., Findings 2026)
ACL
- Kang He, Yuzhe Ding, Rao Fu, Yukang Feng, Kaipeng Zhang, Yiming Liu, Fei Li, Chong Teng, and Donghong Ji. 2026. Uncertainty-Calibrated Elastic Alignment for Multimodal Sentiment Analysis with Missing Modalities. In Findings of the Association for Computational Linguistics: ACL 2026, pages 5268–5288, San Diego, California, United States. Association for Computational Linguistics.