SAGCN: A syntactic aware multi-branch graph attention network with structural bias for aspect sentiment triplet extraction

Aspect-based sentiment triplet extraction (ASTE), a newly developed and complex subtask within aspect-based sentiment analysis, focuses on detecting aspect terms, opinion terms, and establishing sentiment polarity from human language, thereby extracting triplets composed of these three elements. Although numerous methods have been developed in previous research to tackle this task, these ASTE methods exhibit weak interactions in constructing contextual representations and overlook the syntactic relationships between aspect terms and opinion terms. Therefore, this paper proposes a syntax-aware multi-branch graph attention network to address this issue. We have designed an efficient approach that integrates new structural biases into pre-trained language models through adapters to enhance the original mappings in self-attention, significantly reducing the parameter requirements and achieving lower latency. Simultaneously, we have devised a syntax-aware attention mechanism that not only discerns edges with varying dependency types as well as those with identical types, learning the representation of each edge in the graph relying on the dependency types of neighboring edges, thereby enabling more accurate graph propagation. Finally, we have designed a special fusion interaction layer that achieves the final text representation by merging different branch features with varying weights. Through a range of tests performed on four widely accessible datasets, it was demonstrated that the introduction of structural bias adapters is both effective and efficient. The proposed method improved the average F1 score by up to 4.11% compared to all baseline models, while also exhibiting good interpretability. Additionally, the experimental results highlighted the robustness and effectiveness of SAGCN, significantly outperforming the compared state-of-the-art baseline models.