विधियों की तुलना करें

	डोमेन-अनुकूली प्रश्नोत्तर	BERT-आधारित वर्गीकरण
क्षेत्र	गहन अधिगम	गहन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2019–2020	2019
प्रवर्तक	Multiple (e.g., Garg et al.; Yue et al.)	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI Language)
प्रकार	Domain adaptation for extractive/generative QA	Pre-trained language model with fine-tuning
मौलिक स्रोत	Garg, S., Vu, T., & Moschitti, A. (2020). TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection. Proceedings of the AAAI Conference on Artificial Intelligence, 34(5), 7780–7788.	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.
उपनाम	DA-QA, domain-adapted QA, domain-specific question answering, cross-domain question answering	BERT classifier, BERT fine-tuning for classification, BERT text classification, BERT-CLS
संबंधित	6	4
सारांश	Domain-adaptive Question Answering (DA-QA) adapts a pre-trained language model — typically BERT or RoBERTa — first trained on general QA benchmarks such as SQuAD to answer questions accurately in a new target domain (e.g., biomedical, legal, financial) where labelled data is scarce. Combining domain-adaptive pre-training with task fine-tuning yields substantially stronger performance than direct fine-tuning alone.	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.