Methods to Enhance BERT in Aspect-Based Sentiment Classification

Abstract

BERT is a widely used pre-trained model in Natural Language Processing tasks, including Aspect-Based sentiment classification. BERT is equipped with sufficient prior language knowledge in the enormous amount of pre-trained model parameters, for which the fine-tuning of BERT has become a critical issue. Previous works mainly focused on specialized downstream networks or additional knowledge to fine-tune the BERT to the sentiment classification tasks. In this paper, we design experiments to find the fine-tuning techniques that can be used by all models with BERT in the Aspect-Based Sentiment Classification tasks. Through these experiments, we verify different feature extraction, regularization, and continual learning methods, then we summarize 8 universally applicable conclusions to enhance the training and performance of the BERT model.