Enhancing Aspect Sentiment Quad Prediction through Dual-Sequence Data Augmentation and Contrastive Learning

Aspect sentiment quad prediction (ASQP) endeavors to analyze four sentiment elements in sentences. Recent studies utilize generative models to achieve this task, yielding commendable outcomes. However, these studies often fall short of fully leveraging the relationships between sentiment elements and have difficulty effectively handling implicit sentiment expressions. Furthermore, this task also confronts the obstacle of data scarcity stemming from the substantial expenses involved in data annotation. To address these limitations, we propose dual-sequence data augmentation to achieve diverse input and target expressions, while we incorporate contrastive learning to instigate the model to distinctly represent the presence or absence of these pivotal features pertaining to implicit aspects and opinion terms. Additionally, we introduce a prediction normalization strategy to refine the produced results. Empirical findings from experiments on four publicly available datasets show the superiority of our method, surpassing multiple baseline approaches and achieving state-of-the-art performance on the benchmark.