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Humans are sophisticated at reading interlocutors' emotions from multimodalsignals, such as speech contents, voice tones and facial expressions. However,machines might struggle to understand various emotions due to the difficulty ofeffectively decoding emotions from the complex interactions between multimodalsignals. In this paper, we propose a multimodal emotion analysis framework,InterMulti, to capture complex multimodal interactions from different views andidentify emotions from multimodal signals. Our proposed framework decomposessignals of different modalities into three kinds of multimodal interactionrepresentations, including a modality-full interaction representation, amodality-shared interaction representation, and three modality-specificinteraction representations. Additionally, to balance the contribution ofdifferent modalities and learn a more informative latent interactionrepresentation, we developed a novel Text-dominated Hierarchical High-orderFusion(THHF) module. THHF module reasonably integrates the above three kinds ofrepresentations into a comprehensive multimodal interaction representation.Extensive experimental results on widely used datasets, (i.e.) MOSEI, MOSI andIEMOCAP, demonstrate that our method outperforms the state-of-the-art.

InterMulti:Multi-view Multimodal Interactions with Text-dominated Hierarchical High-order Fusion for Emotion Analysis