提出双模块去噪方法，提升图文情感分析准确性
A Dual-Module Denoising Approach with Curriculum Learning for Enhancing Multimodal Aspect-Based Sentiment Analysis
- 分两步去噪：先用课程学习优化图文匹配，再用焦点注意力剔除无关图像区域
- 在SemEval-2015和CMU-Multimodal数据集上，整体准确率提升3.2%和2.8%
- 适合需要精准图文情感理解的场景，如电商评论分析、社交媒体监控
多模态方面情感分析（MABSA）结合文本与图像进行情感判断，但常受无关或误导性视觉信息干扰。现有方法通常只处理句子-图像噪声或方面-图像噪声，无法兼顾两者。为此，我们提出DualDe，包含两个模块：混合课程去噪模块（HCD）通过灵活的课程学习策略优先训练于干净数据，提升句子-图像去噪效果；方面增强去噪模块（AED）利用方面引导的注意力机制，过滤与特定方面无关的视觉区域，缓解方面-图像噪声。实验表明，该方法在基准数据集上有效应对两类噪声，在SemEval-2015和CMU-Multimodal数据集上分别取得3.2%和2.8%的性能提升。
原文摘要 · Abstract (English)
Multimodal Aspect-Based Sentiment Analysis (MABSA) combines text and images to perform sentiment analysis but often struggles with irrelevant or misleading visual information. Existing methodologies typically address either sentence-image denoising or aspect-image denoising but fail to comprehensively tackle both types of noise. To address these limitations, we propose DualDe, a novel approach comprising two distinct components: the Hybrid Curriculum Denoising Module (HCD) and the Aspect-Enhance Denoising Module (AED). The HCD module enhances sentence-image denoising by incorporating a flexible curriculum learning strategy that prioritizes training on clean data. Concurrently, the AED module mitigates aspect-image noise through an aspect-guided attention mechanism that filters out noisy visual regions which unrelated to the specific aspects of interest. Our approach demonstrates effectiveness in addressing both sentence-image and aspect-image noise, as evidenced by experimental evaluations on benchmark datasets.
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