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We tackle the crucial challenge of fusing different modalities of featuresfor multimodal sentiment analysis. Mainly based on neural networks, existingapproaches largely model multimodal interactions in an implicit andhard-to-understand manner. We address this limitation with inspirations fromquantum theory, which contains principled methods for modeling complicatedinteractions and correlations. In our quantum-inspired framework, the wordinteraction within a single modality and the interaction across modalities areformulated with superposition and entanglement respectively at differentstages. The complex-valued neural network implementation of the frameworkachieves comparable results to state-of-the-art systems on two benchmarkingvideo sentiment analysis datasets. In the meantime, we produce the unimodal andbimodal sentiment directly from the model to interpret the entangled decision.

Quantum-inspired Multimodal Fusion for Video Sentiment Analysis