<p>Utilizing pre-trained generation models for predicting sentiment elements has recently shown significant advancements in aspect sentiment quad prediction benchmarks. However, these models overlook the significance of syntactic information, which have proven to be effective in previous extraction-based approaches. Different from extraction-based models, efficiently encoding the syntactic structure in generation model is challenging because such models are pretrained on natural language, and modeling structured data may lead to catastrophic forgetting of distributional knowledge. In this study, we propose an innovative structure-aware framework that explicitly encodes the syntactic structure into the pre-trained generation model while preserving its original distributional knowledge.</p>