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Multimodal Sentiment Analysis With Two-Phase Multi-Task Learning
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DOI：10.1109/TASLP.2022.3178204.png)
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摘要
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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 proposes a multimodal framework named Two-Phase Multi-task Sentiment Analysis (TPMSA). It 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 conducted experiments on two multimodal benchmark datasets, CMU-MOSI and CMU-MOSEI. The results show that our TPMSA model outperforms the current SOTA method on both datasets across most of the metrics, clearly showing our proposed method's effectiveness.
Keyword:
Bit error rate
Task analysis
Multitasking
Visualization
Sentiment analysis
Training
Transformers
BERT
Multimodal sentiment analysis
multi-task
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5.1
论文数:
2.6K
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1.1W
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