Exploring Graph Pre-training for Aspect-based Sentiment Analysis
Xiaoyi Bao, Zhongqing Wang, Guodong Zhou · 2023
Existing studies tend to extract the sentiment elements in a generative manner in order to avoid complex modeling.Despite their effectiveness, they ignore importance of the relationships between sentiment elements that could be crucial, making the large pre-trained generative models sub-optimal for modeling sentiment knowledge.Therefore, we introduce two pre-training paradigms to improve the generation model by exploring graph pre-training that targeting to strengthen the model in capturing the elements' relationships.Specifically, We first employ an Element-level Graph Pretraining paradigm, which is designed to improve the structure awareness of the generative model.Then, we design a Task-level Graph Pre-training paradigm to make the generative model generalizable and robust against various irregular sentiment quadruples.Extensive experiments show the superiority of our proposed method, and validate the correctness of our motivation.Our code can be found in https://github.com/HoraceXIaoyiBao/ EGP4ABSA-EMNLP2023.