Transition-based Opinion Generation for Aspect-based Sentiment Analysis

Recently, the use of pre-trained generation models for extracting sentiment elements has resulted in significant advancements in aspectbased sentiment analysis benchmarks. However, these approaches often overlook the importance of explicitly modeling structure among sentiment elements. To address this limitation, we present a study that aims to integrate general pre-trained sequence-tosequence language models with a structureaware transition-based approach. Therefore, we propose a transition system for opinion tree generation, designed to better exploit pretrained language models for structured finetuning. Our proposed transition system ensures the structural integrity of the generated opinion tree. By leveraging pre-trained generation models and simplifying the transition set, we are able to maximize the accuracy of opinion tree generation. Extensive experiments show that our model significantly advances the state-of-the-art performance on several benchmark datasets. In addition, the empirical studies also indicate that the proposed opinion tree generation with transition system is more effective in capturing the sentiment structure than other generation models.