一种结合课程学习的双模块去噪方法，用于增强多模态方面级情感分析
A Dual-Module Denoising Approach with Curriculum Learning for Enhancing Multimodal Aspect-Based Sentiment Analysis
AI总结：
针对多模态方面级情感分析中存在的句子-图像和方面-图像两类噪声问题，提出包含混合课程去噪与方面增强去噪双模块的DualDe方法，经基准数据集实验验证可有效去噪提升性能。
摘要：
多模态方面级情感分析（MABSA）结合文本与图像开展情感分析，但常受无关或误导性视觉信息的困扰。现有方法通常仅处理句子-图像去噪或方面-图像去噪，无法全面应对两类噪声。为解决这些局限，我们提出DualDe——一种包含两个独立组件的新方法：混合课程去噪模块（HCD）和方面增强去噪模块（AED）。HCD模块通过引入灵活的课程学习策略增强句子-图像去噪，该策略优先使用干净数据训练。同时，AED模块通过方面引导的注意力机制缓解方面-图像噪声，过滤掉与特定关注方面无关的噪声视觉区域。在基准数据集上的实验评估表明，我们的方法能有效处理句子-图像和方面-图像两类噪声。
英文摘要：
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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