Adaptive Data Augmentation for Aspect Sentiment Quad Prediction
Wenyuan Zhang Xinghua Zhang Shiyao Cui Kun Huang Xuebin Wang Tingwen Liu
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.