Knowledge-Augmented Graph Convolutional Network for Aspect Sentiment Triplet Extraction

Aspect Sentiment Triplet Extraction (ASTE) aims to jointly identify aspect terms, opinion terms, and their associated sentiment polarities. Existing approaches, such as tagging or span-based modeling, often struggle with complex aspect–opinion interactions and long-distance dependencies. We propose a Knowledge-Augmented Graph Convolutional Network (KMG-GCN) that represents a sentence as a multi-channel graph integrating syntactic dependencies, part-of-speech tags, and positional relations. An adjacency tensor is constructed via a biaffine attention mechanism, while a multi-anchor triplet learning strategy with orthogonal projection enhances representation disentanglement. Furthermore, a pairwise refinement module explicitly models aspect–opinion associations, improving robustness against overlapping triplets. Experiments on multiple benchmarks demonstrate that KMG-GCN achieves state-of-the-art performance with improved efficiency and generalization.