Progressive Prompt Generative Graph Convolutional Network for Aspect-Based Sentiment Quadruple Prediction

Aspect-based sentiment quadruple prediction has important application value in the current information age. There are often implicit expressions and multi-level semantic relationships in sentences, making accurate prediction for existing methods still a complex and challenging task. To address the above problems, this paper proposes the Progressive Prompt-Driven Generative Graph Convolutional Network for Aspect-Based Sentiment Quadruple Prediction (ProPGCN). Firstly, a progressive prompt module is proposed. The module uses progressive prompt templates to generate paradigm expressions of corresponding orders and introduces third-order element prompt templates to associate high-order semantics in sentences, providing a bridge for modeling the final global semantics. Secondly, a graph convolutional relation-enhanced reasoning module is designed, which can make full use of contextual dependency information to enhance the recognition of implicit aspects and implicit opinions. In addition, a graph convolutional aggregation strategy is constructed. The strategy uses graph convolutional networks to aggregate adjacent node information and correct conflicting implicit logical relationships. Finally, experimental results show that the ProPGCN model can achieve state-of-the-art performance. Specifically, our ProPGCN model achieves overall F1 scores of 65.04% and 47.89% on the Restaurant and Laptop datasets, respectively, which represent improvements of +0.83% and +0.61% over the previous strongest generative baseline.