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

Nguyen Van Doan, Dat Tran Nguyen, Cam-Van Thi Nguyen

TL;DR

DualDe tackles noisy multimodal signals in MABSA by coupling Hybrid Curriculum Denoising (HCD) with Aspect-Enhance Denoising (AED). HCD guides training with a composite difficulty $d^c_i = α d^l_i + (1 - α) d^s_i$ (α = 0.8) and a competence curve $p(t)$ to progressively expose the model to harder samples; AED employs an aspect-guided attention and a GCN over a weighted association matrix to filter non-essential visual regions and text tokens. On Twitter2015 and Twitter2017, DualDe achieves state-of-the-art results across JMASA, MASC, and MATE, with gains in precision, recall, and F1. The work demonstrates robust cross-modal denoising and improved cross-modal alignment with implications for real-world noisy multimodal sentiment analysis.

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.

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

TL;DR

DualDe tackles noisy multimodal signals in MABSA by coupling Hybrid Curriculum Denoising (HCD) with Aspect-Enhance Denoising (AED). HCD guides training with a composite difficulty (α = 0.8) and a competence curve to progressively expose the model to harder samples; AED employs an aspect-guided attention and a GCN over a weighted association matrix to filter non-essential visual regions and text tokens. On Twitter2015 and Twitter2017, DualDe achieves state-of-the-art results across JMASA, MASC, and MATE, with gains in precision, recall, and F1. The work demonstrates robust cross-modal denoising and improved cross-modal alignment with implications for real-world noisy multimodal sentiment analysis.

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.

Paper Structure

This paper contains 24 sections, 13 equations, 5 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Multimodal Aspect-based Sentiment Analysis
  4. Curriculum Learning
  5. Methodology
  6. Hybrid Curriculum Denoising Module (HCD)
  7. Similarity Difficulty Metric
  8. Model loss Diffculty Metric
  9. Comprehensive Difficulty Metric
  10. Curriculum Training
  11. Aspect-Enhance Denoising Module (AED)
  12. Aspect-Based Enhance Sentic Attention(AESA)
  13. Weighted Association Matrix
  14. Graph Convolutional Network (GCN)
  15. Prediction and Loss Function
  16. ...and 9 more sections

Figures (5)

  • Figure 1: Illustration of Sentence-Image Denoising and Aspect-Image Denoising. Sentence-Image Denoising classifies an image as clean if it is relevant to the overall sentence meaning. Aspect-Image Denoising identifies regions as noise (e.g., blurred areas) when they lack strong relevance to any specific aspect.
  • Figure 2: Model Overview
  • Figure 3: Illustrate the curve of the competence function $p(t)$ and the corresponding amount of selected data at epoch $t$.
  • Figure 4: Illustration Contribution Ratio Coefficient Test
  • Figure 5: The figure illustrates instances where sentence-image noise and aspect-image noise impact the effectiveness of sentiment analysis. The easy sample features a clear alignment between the sentence and image, enhancing sentiment detection, while the hard sample involves a blurry image with minimal relevance to the sentence's aspects, complicating accurate sentiment evaluation.