Generative aspect-based sentiment analysis with a grid tagging matching auxiliary task

Abstract

Aspect-based sentiment analysis has gained significant attention in recent years. Particularly, the employment of generative models to address the Aspect-Category-Opinion-Sentiment (ACOS) quadruple extraction task has emerged as a prominent research focus. However, existing studies have not thoroughly explored the inherent connections among sentiment elements, which could potentially enhance the extraction capabilities of the model. To this end, we propose a novel Generative Model with a Grid Tagging Matching auxiliary task, dubbed as GM-GTM. First, to fully harness the logical interaction flourishing within sentiment elements, a newly output template is designed for generative extraction task, which conforms to causality and human intuition. Besides, we technically introduce a grid tagging matching auxiliary task. Specifically, a grid tagging matrix is designed, in which various tags are defined to represent different relationships among sentiment elements. In this way, a comprehensive understanding of the relationships among sentiment elements is obtained. Consequently, the model’s reasoning ability is enhanced, enabling it to make more informed inferences regarding new sentiment elements based on existing ones. Extensive experimental results on ACOS datasets demonstrated the superior performance of our model compared with existing state-of-the-art methods.