SGPT: Semantic Graphs based Pre-training for Aspect-based Sentiment Analysis
This work addresses the need for better sentiment analysis models by incorporating sentimental information into pre-training, though it appears incremental as it builds on existing pre-trained language models.
The authors tackled the problem of aspect-based sentiment analysis by enhancing pre-trained language models with semantic graphs to incorporate sentimental information, resulting in a model that outperforms strong baselines on several downstream tasks.
Previous studies show effective of pre-trained language models for sentiment analysis. However, most of these studies ignore the importance of sentimental information for pre-trained models.Therefore, we fully investigate the sentimental information for pre-trained models and enhance pre-trained language models with semantic graphs for sentiment analysis.In particular, we introduce Semantic Graphs based Pre-training(SGPT) using semantic graphs to obtain synonym knowledge for aspect-sentiment pairs and similar aspect/sentiment terms.We then optimize the pre-trained language model with the semantic graphs.Empirical studies on several downstream tasks show that proposed model outperforms strong pre-trained baselines. The results also show the effectiveness of proposed semantic graphs for pre-trained model.