A Dual-Module Denoising Approach with Curriculum Learning for Enhancing Multimodal Aspect-Based Sentiment Analysis
This addresses noise in multimodal sentiment analysis for applications like social media analysis, but it is incremental as it builds on existing denoising methods.
The paper tackled the problem of irrelevant or misleading visual information in Multimodal Aspect-Based Sentiment Analysis by proposing DualDe, a dual-module approach with curriculum learning, which improved performance on benchmark datasets.
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.