手法を比較

	ファインチューニングされた質問応答	BERTベースの分類
分野	深層学習	深層学習
系統	Machine learning	Machine learning
提唱年	2016–2019	2019
提唱者	Devlin et al. (BERT); Rajpurkar et al. (SQuAD benchmark)	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI Language)
種類	Transfer learning / fine-tuning for extractive or generative QA	Pre-trained language model with fine-tuning
原典	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.	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.
別名	fine-tuned QA, neural QA with fine-tuning, extractive QA fine-tuning, reading comprehension fine-tuning	BERT classifier, BERT fine-tuning for classification, BERT text classification, BERT-CLS
関連	5	4
概要	Fine-Tuned Question Answering adapts a large pre-trained language model — such as BERT, RoBERTa, or a GPT-family model — to answer natural-language questions over a given context passage or knowledge base. The model learns to locate answer spans or generate free-form answers by continuing training on labeled QA pairs after general-purpose pre-training.	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.