MetaCert: Metabolic Attention Network Utilizing Uncertainty Estimation for Multimodal Aspect-Category-Sentiment Triple Extraction

Abstract

Multimodal Aspect-Category-Sentiment Triple Extraction (MACSTE) is a highly complex subtask within Multimodal Aspect-Based Sentiment Analysis (MABSA), requiring simultaneous attribute extraction and sentiment polarity prediction from image-text pairs. While existing research often emphasizes modality fusion and alignment, it frequently neglects the design of information flow pathways, leading to suboptimal utilization of complementary information. Additionally, modality-specific noise may compromise the robustness and accuracy of multimodal classification, with traditional filtering methods often degrading data quality. To overcome these challenges, we propose the Metabolic Attention Network Utilizing Uncertainty Estimation (MetaCert). MetaCert integrates two key components: the Metabolic Attention Mechanism (MAM), inspired by bio-chemical metabolic networks and enhanced by cross-attention for improved information exchange; and the Uncertainty Estimation Network (UEN), which optimizes the semantic contributions of each modality while preserving data integrity, thereby enhancing classification accuracy. Our approach achieves state-of-the-art (SOTA) results on the TWITTER-15 and TWITTER-17 datasets.