Wenfang Wu
2023
Few-shot Joint Multimodal Aspect-Sentiment Analysis Based on Generative Multimodal Prompt
Xiaocui Yang
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Shi Feng
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Daling Wang
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Qi Sun
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Wenfang Wu
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Yifei Zhang
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Pengfei Hong
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Soujanya Poria
Findings of the Association for Computational Linguistics: ACL 2023
We have witnessed the rapid proliferation of multimodal data on numerous social media platforms. Conventional studies typically require massive labeled data to train models for Multimodal Aspect-Based Sentiment Analysis (MABSA). However, collecting and annotating fine-grained multimodal data for MABSA is tough. To alleviate the above issue, we perform three MABSA-related tasks with quite a small number of labeled multimodal samples. We first build diverse and comprehensive multimodal few-shot datasets according to the data distribution. To capture the specific prompt for each aspect term in a few-shot scenario, we propose a novel Generative Multimodal Prompt (GMP) model for MABSA, which includes the Multimodal Encoder module and the N-Stream Decoders module. We further introduce a subtask to predict the number of aspect terms in each instance to construct the multimodal prompt.Extensive experiments on two datasets demonstrate that our approach outperforms strong baselines on two MABSA-related tasks in the few-shot setting.
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Co-authors
- Xiaocui Yang 1
- Shi Feng 1
- Daling Wang 1
- Qi Sun 1
- Yifei Zhang 1
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