Generative Aspect-based Sentiment Quadruple Prediction based on Multi-Order Prompting

Recently, generative aspect-level sentiment quadruple prediction (ASQP) methods based on pre-trained language models have made significant progress. However, some challenges remain in extracting and recognizing complex sentiment elements from semantically rich sentences, limiting the generalization and adaptability of unidirectional generative models in aspect-level sentiment analysis. To overcome this limitation, this paper proposes a Generative Aspect-Based Sentiment Quadruple Prediction Model based on Multi-Order Prompting (GenMOP). The model draws on the concept of prompt learning and introduces a multi-order prompting strategy, which breaks the traditional framework of a single generative order and enhances the flexibility and adaptability of the model. Furthermore, we integrate a quadruple quantity-aware module and a multi-view uncertainty-aware module based on a basic generative architecture, not only providing the model with more fine-grained information about the quadruple quantity but also improving the prediction accuracy through uncertainty estimation. The extensive experiments show that the GenMOP method achieves excellent performance in the ASQP task. On the four benchmark datasets including Rest15, Rest16, Rest and Lap, our model achieves F1 score improvements of 1.26%, 0.23%, 0.28% and 2.07%, respectively, compared to existing state-of-the-arts, demonstrating its effectiveness and superiority in dealing with the joint extraction of multiple sentiment elements of the ASQP model.