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We have witnessed the rapid proliferation of multimodal data on numeroussocial media platforms. Conventional studies typically require massive labeleddata to train models for Multimodal Aspect-Based Sentiment Analysis (MABSA).However, collecting and annotating fine-grained multimodal data for MABSA istough. To alleviate the above issue, we perform three MABSA-related tasks withquite a small number of labeled multimodal samples. We first build diverse andcomprehensive multimodal few-shot datasets according to the data distribution.To capture the specific prompt for each aspect term in a few-shot scenario, wepropose a novel Generative Multimodal Prompt (GMP) model for MABSA, whichincludes the Multimodal Encoder module and the N-Stream Decoders module. Wefurther introduce a subtask to predict the number of aspect terms in eachinstance to construct the multimodal prompt. Extensive experiments on twodatasets demonstrate that our approach outperforms strong baselines on twoMABSA-related tasks in the few-shot setting.

Few-shot Joint Multimodal Aspect-Sentiment Analysis Based on Generative Multimodal Prompt