Improving BERT-Based Text Classification With Auxiliary Sentence and Domain Knowledge

• Published in: IEEE access Vol. 7; pp. 176600 - 176612
• Main Authors: Yu, Shanshan, Su, Jindian, Luo, Da
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: bidirectional encoder representations from transformer; Bit error rate; Classification; Context modeling; Datasets; Domains; language model; Natural language processing; neural networks; Sentences; State vectors; Task analysis; Text categorization; text classification; Training; Training data
• DOI: 10.1109/ACCESS.2019.2953990
• EISSN: 2169-3536

General language model BERT pre-trained on cross-domain text corpus, BookCorpus and Wikipedia, achieves excellent performance on a couple of natural language processing tasks through the way of fine-tuning in the downstream tasks. But it still lacks of task-specific knowledge and domain-related knowledge for further improving the performance of BERT model and more detailed fine-tuning strategy analyses are necessary. To address these problem, a BERT-based text classification model BERT4TC is proposed via constructing auxiliary sentence to turn the classification task into a binary sentence-pair one, aiming to address the limited training data problem and task-awareness problem. The architecture and implementation details of BERT4TC are also presented, as well as a post-training approach for addressing the domain challenge of BERT. Finally, extensive experiments are conducted on seven public widely-studied datasets for analyzing the fine-tuning strategies from the perspectives of learning rate, sequence length and hidden state vector selection. After that, BERT4TC models with different auxiliary sentences and post-training objectives are compared and analyzed in depth. The experiment results show that BERT4TC with suitable auxiliary sentence significantly outperforms both typical feature-based methods and fine-tuning methods, and achieves new state-of-the-art performance on multi-class classification datasets. For binary sentiment classification datasets, our BERT4TC post-trained with suitable domain-related corpus also achieves better results compared with original BERT model.