Aspect sentiment quad prediction (ASQP) aims to predict the quad sentiment
elements for a given sentence, which is a critical task in the field of
aspect-based sentiment analysis. However, the data imbalance issue has not
received sufficient attention in ASQP task. In this paper, we divide the issue
into two-folds, quad-pattern imbalance and aspect-category imbalance, and
propose an Adaptive Data Augmentation (ADA) framework to tackle the imbalance
issue. Specifically, a data augmentation process with a condition function
adaptively enhances the tail quad patterns and aspect categories, alleviating
the data imbalance in ASQP. Following previous studies, we also further explore
the generative framework for extracting complete quads by introducing the
category prior knowledge and syntax-guided decoding target. Experimental
results demonstrate that data augmentation for imbalance in ASQP task can
improve the performance, and the proposed ADA method is superior to naive data
oversampling.

该研究提出了自适应数据增强（ADA）框架来解决情感分析中的数据不平衡问题，并探索了生成框架用于提取完整的情感四元组。实验结果表明，自适应数据增强方法对于改善情感分析任务中的数据不平衡问题具有优势。