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UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion Recognition
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UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion Recognition
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Multimodal sentiment analysis (MSA) and emotion recognition in conversation (ERC) are key research topics for computers to understand human behaviors. From a psychological perspective, emotions are the expression of affect or feelings during a short period, while sentiments are formed and held for a longer period. However, most existing works study sentiment and emotion separately and do not fully exploit the complementary knowledge behind the two. In this paper, we propose a multimodal sentiment knowledge-sharing framework (UniMSE) that unifies MSA and ERC tasks from features, labels, and models. We perform modality fusion at the syntactic and semantic levels and introduce contrastive learning between modalities and samples to better capture the difference and consistency between sentiments and emotions. Experiments on four public benchmark datasets, MOSI, MOSEI, MELD, and IEMOCAP, demonstrate the effectiveness of the proposed method and achieve consistent improvements compared with state-of-the-art methods.
Forward citations
Cited by 4 Pith papers
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Missing-by-Design learns property-aware embeddings and uses saliency-driven Gaussian updates to produce machine-verifiable certificates that remove a chosen modality without full retraining.
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ModalImmune: Immunity Driven Unlearning via Self Destructive Training
ModalImmune enforces modality immunity in multimodal models by controlled collapse of input channels during training using adaptive regularizers and meta-optimization.
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A Conflict-Aware Penalty and Statistical Loss Framework for Balancing Modalities and Enhancing Stability in Multimodal Sentiment Analysis
Introduces CP and SL to balance modalities and stabilize training in MSA, reporting SOTA results on CMU-MOSI with component ablations.
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