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

TL;DR

This paper introduces DualDe, a dual-module approach with curriculum learning for multimodal aspect-based sentiment analysis, effectively reducing noise from irrelevant images and improving sentiment prediction accuracy.

Contribution

The paper proposes a novel dual-module framework with curriculum learning and aspect-guided attention to comprehensively denoise multimodal sentiment data.

Findings

Effective noise reduction in multimodal sentiment analysis

Improved sentiment prediction accuracy on benchmark datasets

Addresses both sentence-image and aspect-image noise

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.