Few-shot Multimodal Sentiment Analysis based on Multimodal Probabilistic Fusion Prompts

• URL: http://arxiv.org/abs/2211.06607v2
• Date: Tue, 1 Aug 2023 07:50:19 GMT
• Title: Few-shot Multimodal Sentiment Analysis based on Multimodal Probabilistic Fusion Prompts
• Authors: Xiaocui Yang, Shi Feng, Daling Wang, Pengfei Hong, Soujanya Poria
• Abstract summary: Multimodal sentiment analysis has gained significant attention due to the proliferation of multimodal content on social media. Existing studies in this area rely heavily on large-scale supervised data, which is time-consuming and labor-intensive to collect. We propose a novel method called Multimodal Probabilistic Fusion Prompts (MultiPoint) that leverages diverse cues from different modalities for multimodal sentiment detection in the few-shot scenario.
• Score: 30.15646658460899
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal sentiment analysis has gained significant attention due to the proliferation of multimodal content on social media. However, existing studies in this area rely heavily on large-scale supervised data, which is time-consuming and labor-intensive to collect. Thus, there is a need to address the challenge of few-shot multimodal sentiment analysis. To tackle this problem, we propose a novel method called Multimodal Probabilistic Fusion Prompts (MultiPoint) that leverages diverse cues from different modalities for multimodal sentiment detection in the few-shot scenario. Specifically, we start by introducing a Consistently Distributed Sampling approach called CDS, which ensures that the few-shot dataset has the same category distribution as the full dataset. Unlike previous approaches primarily using prompts based on the text modality, we design unified multimodal prompts to reduce discrepancies between different modalities and dynamically incorporate multimodal demonstrations into the context of each multimodal instance. To enhance the model's robustness, we introduce a probabilistic fusion method to fuse output predictions from multiple diverse prompts for each input. Our extensive experiments on six datasets demonstrate the effectiveness of our approach. First, our method outperforms strong baselines in the multimodal few-shot setting. Furthermore, under the same amount of data (1% of the full dataset), our CDS-based experimental results significantly outperform those based on previously sampled datasets constructed from the same number of instances of each class.

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