Beyond Static Alignment: Adaptive Arbitration for Semantic Incongruence in Semi-Supervised Multimodal Sentiment Analysis (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods for semantic incongruence in sentiment analysis are limited by label-limited settings. |
| Approach: | They propose a framework for semi-supervised multimodal sentiment analysis that emphasizes stable cross-modal representations and reliable supervision. |
| Outcome: | The proposed framework outperforms state-of-the-art methods under label-limited settings. |
Similar Papers
Improving Multimodal Sentiment Analysis: Supervised Angular margin-based Contrastive Learning for Enhanced Fusion Representation (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for multimodal sentiment analysis focus on forming positive and negative pairs, neglecting the variation in sentiment scores within the same class. |
| Approach: | They propose a framework to enhance discrimination and generalizability of the multimodal representation and overcome biases in the fusion vector’s modality. |
| Outcome: | The proposed model improves discrimination and generalizability of the multimodal representation and overcomes biases in the fusion vector’s modality. |
Learning Language-guided Adaptive Hyper-modality Representation for Multimodal Sentiment Analysis (2023.emnlp-main)
Copied to clipboard
| Challenge: | Multimodal Sentiment Analysis (MSA) is effective when using rich information from multiple sources, but the potential sentiment-irrelevant information across modalities may hinder the performance from being further improved. |
| Approach: | They propose an Adaptive Language-guided Multimodal Transformer (ALMT) that learns an irrelevance/conflict-suppressing representation from visual and audio features under guidance of language features at different scales. |
| Outcome: | The proposed model achieves state-of-the-art on several popular datasets and an abundance of ablation shows the effectiveness of the proposed model. |
Uncertainty-Calibrated Elastic Alignment for Multimodal Sentiment Analysis with Missing Modalities (2026.findings-acl)
Copied to clipboard
Kang He, Yuzhe Ding, Rao Fu, Yukang Feng, Kaipeng Zhang, Yiming Liu, Fei Li, Chong Teng, Donghong Ji
| Challenge: | Existing methods for multimodal sentiment analysis are often dynamically incomplete. |
| Approach: | They propose a new uncertainty-calibrated elastic alignment framework to address these issues by employing probabilistic imputation to capture cross-modal ambiguity and leverage the estimated uncertainty to drive elastic alignment. |
| Outcome: | The proposed framework outperforms state-of-the-art models in multiple benchmarks and consistently outperformed existing models. |
Proxy-Driven Robust Multimodal Sentiment Analysis with Incomplete Data (2025.acl-long)
Copied to clipboard
| Challenge: | Existing studies focus on optimizing model structures to handle uncertain missingness, but models still face challenges when dealing with uncertain missing data. |
| Approach: | They propose a data-centric robust multimodal sentiment analysis method, Proxy-Driven Robust Multimodal Fusion, which maps unimodal data to the latent space of Gaussian distributions to capture core features and structure. |
| Outcome: | The proposed method outperforms existing models in noise resistance and achieves state-of-the-art performance on multiple benchmark datasets. |
CLASP: Cross-modal Alignment Using Pre-trained Unimodal Models (2024.findings-acl)
Copied to clipboard
| Challenge: | Recent advances in speech-text pretraining rely on parallel speech- text data . however, data accessibility is a challenge due to the limited data available. |
| Approach: | They propose a framework for jointly performing speech and text processing without parallel corpora during pre-training but only downstream. |
| Outcome: | The proposed framework extracts distinct representations for speech and text, aligning them effectively in a newly defined space using a multi-level contrastive learning mechanism. |
Tackling Modality Heterogeneity with Multi-View Calibration Network for Multimodal Sentiment Detection (2023.acl-long)
Copied to clipboard
| Challenge: | Existing studies focus on fusing different features but ignore the challenge of modality heterogeneity. |
| Approach: | They propose a text-guided fusion module with novel Sparse-Attention to reduce the negative impacts of redundant visual elements and a sentiment-based congruity constraint task to calibrate the feature shift in the representation space. |
| Outcome: | The proposed model is competitive against existing methods and achieves state-of-the-art results on two public benchmark datasets. |
Beyond Polarity: Continuous Affect-Enhanced Multimodal Aspect-Based Sentiment Classification (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for multimodal aspect-based sentiment classification exploit discrete polarity patterns and generic visual embeddings. |
| Approach: | They propose a Valence–Arousal–Dominance(VAD)-Enhanced MABSC framework that integrates VAD signals into multimodal sentiment reasoning and learns emotion-sensitive image representations. |
| Outcome: | The proposed framework brings VAD signals into multimodal sentiment reasoning and learns emotion-sensitive image representations. |
Improving Multimodal fusion via Mutual Dependency Maximisation (2021.emnlp-main)
Copied to clipboard
| Challenge: | Multimodal sentiment analysis is a trending area of research, and multimodal fusion is one of its most active topics. |
| Approach: | They propose to use modality-based penalties to measure dependency between models to improve accuracy. |
| Outcome: | The proposed methods improve accuracy on two well-known sentiment analysis datasets by 4.3 on the proposed models and by-product includes a statistical network which can interpret the high dimensional representations learnt by the model. |
SCOPE: Preserving Modality-Specific Cues to Mitigate Modality Laziness in Multimodal Learning (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to learning multimodal representations emphasize shared semantics and overlook modality-specific cues. |
| Approach: | They propose a framework for learning complete multimodal representations using shared and practical cues. |
| Outcome: | SCOPE outperforms SOTA benchmarks on four datasets and achieves 27.10% accuracy improvement. |
Sentiment Word Aware Multimodal Refinement for Multimodal Sentiment Analysis with ASR Errors (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing models for multimodal sentiment analysis are limited in their capacity to be deployed in the real world. |
| Approach: | They propose a model that can dynamically refine erroneous sentiment words by leveraging multimodal sentiment clues. |
| Outcome: | The proposed model surpasses the state-of-the-art models on three datasets. |