Multimodal Sentiment Analysis With Mutual Information-Based Disentangled Representation Learning

Multimodal sentiment analysis seeks to utilize various types of signals to identify underlying emotions and sentiments. A key challenge in this field lies in multimodal representation learning, which aims to develop effective methods for integrating multimodal features into cohesive representations. Recent advancements include two notable approaches: one focuses on decomposing multimodal features into modality-invariant and -specific components, while the other emphasizes the use of mutual information to enhance the fusion of modalities. Both strategies have demonstrated effectiveness and yielded remarkable results. In this paper, we propose a novel learning framework that combines the strengths of these two approaches, termed mutual information-based disentangled multimodal representation learning. Our approach involves estimating different types of information during feature extraction and fusion stages. Specifically, we quantitatively assess and adjust the proportions of modality-invariant, -specific, and -complementary information during feature extraction. Subsequently, during fusion, we evaluate the amount of information retained by each modality in the fused representation. We employ mutual information or conditional mutual information to estimate each type of information content. By reconciling the proportions of these different types of information, our approach achieves state-of-the-art performance on popular sentiment analysis benchmarks, including CMU-MOSI and CMU-MOSEI.