K. Kotecha

DiMoE: Disentangled Representation Learning with Mixture of Experts Fusion for Sentiment Intensity Prediction and Emotion Classification

DiMoE is proposed, a representation-first framework that integrates feature disentanglement with sparse Mixture of Experts (MoE) fusion, indicating that explicit modality disentanglement combined with Mixture of Experts fusion yields effective multimodal affective representations.