Hybrid Quantum-Classical Neural Network for Multimodal Multitask Sarcasm, Emotion, and Sentiment Analysis

Sarcasm detection in unimodal or multimodal setting is a very complex task. Sarcasm, emotion, and sentiment are related to each other, and hence any multitask model could be an effective way to leverage the interdependence among these tasks. In order to better represent these clandestine associations, we avoid solely relying on traditional machine learning methods to encode the relationships between the modalities. In this article, we propose a hybrid quantum model that banks upon the low computational complexity and robust representational power of a variational quantum circuit (VQC) and the tried and tested dense neural network to tackle sentiment, emotion, and sarcasm classification simultaneously. We empirically establish that the quantum properties like superposition, entanglement, and interference will better capture and replicate not only the cross-modal interactions between text, acoustics, and visuals but also the correlations between the three responses. We consider the extended MUStARD dataset to evaluate our proposed hybrid model. The results show that our proposed hybrid quantum framework yields more promising results for the primary task of sarcasm detection with the help of the two secondary classification tasks, <italic>viz.</italic> sentiment and emotion.