AOBERT: All-modalities-in-One BERT for multimodal sentiment analysis

Multimodal sentiment analysis utilizes various modalities such as Text, Vision and Speech to predict sentiment. As these modalities have unique characteristics, methods have been developed for fusing features. However, the overall modality characteristics are not guaranteed, because traditional fusion methods have some loss of intramodality and inter-modality. To solve this problem, we introduce a single-stream transformer, All-modalities-inOne BERT (AOBERT). The model is pre-trained on two tasks simultaneously: Multimodal Masked Language Modeling (MMLM) and Alignment Prediction (AP). The dependency and relationship between modalities can be determined using two pre-training tasks. AOBERT achieved state-of-the-art results on the CMU-MOSI, CMUMOSEI, and UR-FUNNY datasets. Furthermore, ablation studies that validated combinations of modalities, effects of MMLM and AP and fusion methods confirmed the effectiveness of the proposed model.

Automated summary

Multimodal sentiment analysis uses various modalities to predict sentiment, but traditional methods suffer from loss of intramodality and inter-modality. AOBERT, a single-stream transformer pre-trained on two tasks, achieves state-of-the-art results and addresses this problem effectively.