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.

翻译：方面情感四元组预测（ASQP）旨在预测给定句子中的四个情感要素，这是基于方面的情感分析领域中的一项关键任务。然而，在ASQP任务中，数据不平衡问题尚未得到充分关注。本文将这一问题分为两类——四元组模式不平衡和方面类别不平衡，并提出一种自适应数据增强（ADA）框架来处理不平衡问题。具体而言，通过带有条件函数的数据增强过程，自适应地增强尾部四元组模式和方面类别，从而缓解ASQP中的数据不平衡。沿用先前的研究，我们还进一步探索了生成式框架，通过引入类别先验知识和语法引导的解码目标来提取完整四元组。实验结果表明，针对ASQP任务中不平衡问题的数据增强能够提升性能，且所提出的ADA方法优于简单的数据过采样。