Multimodal Sentiment Analysis With Two-Phase Multi-Task Learning

Multimodal Sentiment Analysis (MSA) is a challenging research area that studies sentiment expressed from multiple heterogeneous modalities. Given those pre-trained language models such as BERT have shown state-of-the-art (SOTA) performance in multiple NLP disciplines, existing models tend to integrate these modalities into BERT and treat the MSA as a single prediction task. However, we find that simply fusing the multimodal features into BERT cannot well establish the power of a strong pre-trained model. Besides, the classification ability of each modality is also suppressed by single-task learning. In this paper, we propose a multimodal framework named Two-Phase Multi-task Sentiment Analysis (TPMSA), which applies a two-phase training strategy to make the most of the pre-trained model and a novel multi-task learning strategy to investigate the classification ability of each representation. We conduct experiments on two multimodal benchmark datasets CMU-MOSI and CMU-MOSEI, the results show that our TPMSA model significantly outperforms the current SOTA method on both datasets across all the metrics, which clearly show the effectiveness of our proposed method. Our code is available at https: //github. com/TPMSA/TPMSA.