Simplified syntax-guided domain-shared representation learning for cross-domain aspect-based sentiment analysis

Cross-domain aspect-based sentiment analysis (ABSA) aims to identify aspect terms and their sentiment polarities in the target domain using annotated data from the source domain. Previous studies have shown that leveraging syntactic knowledge can partially bridge domain gaps. However, routine syntactic labels are diverse and fail to directly reflect the connection between aspects and opinions. This limitation hinders their ability to assist the model in perceiving aspects-sentiments in different domains. To overcome this challenge, we present a cross-domain ABSA approach based on simplified syntactic-guided representation learning. Initially, we design a dependency syntax simplification strategy that unifies syntactic labels of aspect terms and their corresponding opinion terms. We employ these simplified syntactic labels to guide the model's self-supervised learning process, thereby obtaining the domain-shared representations from unlabeled data in both the source and target domains. Using the domain-shared representation, we train the model on annotated data from the source domain and then apply it to make predictions in the target domain. Experimental results across ten transfer tasks using four public datasets demonstrate that our proposed method consistently outperforms other baseline models.