Improve Chinese Aspect Sentiment Quadruplet Prediction via Instruction Learning Based on Large Generate Models

Aspect-Based Sentiment Analysis (ABSA) is a fundamental area of research in Natural Language Processing (NLP). Within ABSA, Aspect Sentiment Quad Prediction (ASQP) aims to accurately identify sentiment quadruplets in target sentences, including aspect terms, aspect categories, corresponding opinion terms, and sentiment polarity. However, most existing research has focused on English datasets. Consequently, while ASQP has seen significant progress in English, the Chinese ASQP task has remained relatively stagnant. Drawing inspiration from methods applied to English ASQP, we propose Chinese generation templates and employ prompt-based instruction learning to enhance the model’s understanding of the task, ultimately improving ASQP performance in the Chinese context. Ultimately, under the same pre-training model configuration, our approach achieved a 5.79% improvement in the F1 score compared to the previously leading method. Furthermore, when utilizing a larger model with reduced training parameters, the F1 score demonstrated an 8.14% enhancement. Additionally, we suggest a novel evaluation metric based on the characteristics of generative models, better-reflecting model generalization. Experimental results validate the effectiveness of our approach.