Affiliations: [a]
School of Software, University of Xinjiang, Xinjiang, China
| [b]
Network and Information Center, University of Xinjiang, Xinjiang, China
| [c]
Internet Information Security Centre, Xinjiang, China
Correspondence:
[*]
Corresponding author. Shengwei Tian, School of Software, University of Xinjiang, Xinjiang, China. E-mail: tianshengwei@163.com.
Abstract: In recent years multimodal sentiment analysis (MSA) has been devoted to developing effective fusion mechanisms and has made advances, however, there are several challenges that have not been addressed adequately: the models make insufficient use of important information (inter-modal relevance and independence information) resulting in additional noise, and the traditional ternary symmetric architecture cannot well solve the problem of uneven distribution of task-related information among modalities. Thus, we propose Mutual Information Maximization and Feature Space Separation and Bi-Bimodal Modality Fusion (MFSBF)framework which effectively alleviates these problems. To alleviate the problem of underutilization of important information among modalities, a mutual information maximization module and a feature space separation module have been designed. The mutual information module maximizes the mutual information between two modalities to retain more relevance (modality-invariant) information, while the feature separation module separates fusion features to prevent the loss of independence(modality-specific) information during the fusion process. As different modalities contribute differently to the model, a bimodal fusion architecture is used, which involves the fusion of two bimodal pairs. The architecture focuses more on the modality that contains more task-ralated information and alleviates the problem of uneven distribution of useful information among modalities. The experiment results of our model on two publicly available datasets (CUM-MOSI and CUM-MOSEI) achieved better or comparable results than previous models, which demonstrate the efficacy of our method.
