方法对比

	基于领域自适应BERT的分类	微调 BERT 分类
领域	深度学习	深度学习
方法族	Machine learning	Machine learning
起源年份	2019–2020	2019
提出者	Gururangan et al. (2020); earlier domain-specific instances include Lee et al. (2020) — BioBERT	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI)
类型	Domain-adaptive pre-training followed by supervised fine-tuning	Pre-trained transformer fine-tuned for classification
开创性文献	Gururangan, S., Marasovic, A., Swayamdipta, S., Lo, K., Beltagy, I., Downey, D., & Smith, N. A. (2020). Don't Stop Pretraining: Adapt Language Models to Domains and Tasks. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), 8342–8360.	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of NAACL-HLT 2019, 4171–4186.
别名	DAPT BERT classification, domain-adaptive pre-training, domain-specific BERT fine-tuning, BERT DAPT	BERT fine-tuning, BERT classifier, fine-tuned BERT, BERT sequence classification
相关	6	5
摘要	Domain-adaptive BERT-based classification extends the standard fine-tuning pipeline by first continuing BERT's masked-language-model pre-training on a large corpus of in-domain unlabeled text, then fine-tuning the adapted model on labeled examples for the target classification task. This two-stage approach closes the vocabulary and distributional gap between BERT's general pre-training corpus and specialized domains such as biomedicine, law, finance, or social-media text.	Fine-Tuned BERT-based Classification adapts a pre-trained BERT transformer to a specific text classification task by adding a lightweight output layer and continuing gradient-based training on labelled examples. It consistently achieves near-state-of-the-art accuracy on sentiment analysis, topic categorisation, intent detection, and other NLP classification tasks with relatively small labelled datasets.