A Dual-Module Denoising Approach with Curriculum Learning for Enhancing Multimodal Aspect-Based Sentiment Analysis

• URL: http://arxiv.org/abs/2412.08489v1
• Date: Wed, 11 Dec 2024 15:53:13 GMT
• Title: A Dual-Module Denoising Approach with Curriculum Learning for Enhancing Multimodal Aspect-Based Sentiment Analysis
• Authors: Nguyen Van Doan, Dat Tran Nguyen, Cam-Van Thi Nguyen,
• Abstract summary: Multimodal Aspect-Based Sentiment Analysis (MABSA) combines text and images to perform sentiment analysis. Existing methodologies address either sentence-image denoising or aspect-image denoising but fail to tackle both types of noise. We propose DualDe, a novel approach comprising two distinct components: the Hybrid Curriculum Denoising Module (HCD) and the Aspect-Enhance Denoising Module (AED)
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• Abstract: 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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