SynSem-ASTE: An Enhanced Multi-Encoder Network for Aspect Sentiment Triplet Extraction With Syntax and Semantics

Aspect Sentiment Triplet Extraction (ASTE) is an essential task in fine-grained opinion mining and sentiment analysis that involves extracting triplets consisting of aspect terms, opinion terms, and their associated sentiment polarities from texts. While prevailing approaches primarily adopt pipeline frameworks or unified tagging schemes for this task, these methods tend to either overlook syntactic structural information and inherent semantic features, or lack explicit mechanisms for integration of syntax and semantics among the triplets' elements. To overcome these shortcomings, we propose an Enhanced Multi-Encoder Network for ASTE with Syntax and Semantics (SynSem-ASTE). Our model innovatively incorporates syntactic information and semantic features derived from syntactic structures and attention weights, which is achieved through the design of a syntax encoder and a semantics encoder. Furthermore, we adopt a grid tagging scheme and an effective inference strategy to extract triplets simultaneously. Extensive evaluations on four benchmark datasets reveal that SynSem-ASTE not only achieves superior performance in terms of the primary metric F1-score, but also exhibits enhanced robustness against variations in model architecture.