方法对比

	可解释问答	[需翻译标题：BERT-based Classification...]
领域	深度学习	深度学习
方法族	Machine learning	Machine learning
起源年份	2016–2020	2019
提出者	Community (DeYoung et al.; Rajpurkar et al.)	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI Language)
类型	Interpretable NLP pipeline	Pre-trained language model with fine-tuning
开创性文献	DeYoung, J., Jain, S., Rajani, N. F., Lehman, E., Xiong, C., Socher, R., & Wallace, B. C. (2020). ERASER: A Benchmark to Evaluate Rationalized NLP Models. In Proceedings of ACL 2020, pp. 4443–4458.	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL-HLT 2019 (pp. 4171–4186). Association for Computational Linguistics.
别名	XQA, interpretable QA, transparent question answering, rationale-based QA	BERT classifier, BERT fine-tuning for classification, BERT text classification, BERT-CLS
相关	5	4
摘要	Explainable Question Answering (XQA) combines neural reading-comprehension models — typically BERT-family transformers — with interpretability methods such as rationale extraction, attention visualization, LIME, or SHAP to reveal why the model selected a particular answer span. The goal is not just accuracy but trustworthy, auditable reasoning that users and domain experts can inspect and verify.	BERT-based Classification fine-tunes Google's Bidirectional Encoder Representations from Transformers model on a labelled text dataset, replacing the generic pre-trained head with a task-specific classification layer. It exploits deep bidirectional context from hundreds of millions of pre-trained parameters to deliver state-of-the-art accuracy on short- and medium-length text classification tasks with relatively modest amounts of labelled data.