KESA: A Knowledge Enhanced Approach To Sentiment Analysis

AbstractThough some recent works focus on injecting sentiment knowledge into pre-trained language models, they usually design mask and reconstruction tasks in the post-training phase. This paper aims to integrate sentiment knowledge in the fine-tuning stage. To achieve this goal, we propose two sentiment-aware auxiliary tasks named sentiment word selection and conditional sentiment prediction and, correspondingly, integrate them into the objective of the downstream task. The first task learns to select the correct sentiment words from the given options. The second task predicts the overall sentiment polarity, with the sentiment polarity of the word given as prior knowledge. In addition, two label combination methods are investigated to unify multiple types of labels in each auxiliary task. Experimental results demonstrate that our approach consistently outperforms baselines (achieving a new state-of-the-art) and is complementary to existing sentiment-enhanced post-trained models.