A fundamental task of fine-grained sentiment analysis is aspect and opinion terms extraction. Supervised-learning approaches have shown good results for this task; however, they fail to scale across domains where labeled data is lacking. Non pre-trained unsupervised domain adaptation methods that incorporate external linguistic knowledge have proven effective in transferring aspect and opinion knowledge from a labeled source domain to un-labeled target domains; however, pre-trained transformer-based models like BERT and RoBERTa already exhibit substantial syntactic knowledge. In this paper, we propose a method for incorporating external linguistic information into a self-attention mechanism coupled with the BERT model. This enables leveraging the intrinsic knowledge existing within BERT together with externally introduced syntactic information, to bridge the gap across domains. We successfully demonstrate enhanced results on three benchmark datasets.