قارن الطرق

	الإجابة على الأسئلة المتكيفة مع المجال	تصنيف قائم على BERT
المجال	التعلم العميق	التعلم العميق
العائلة	Machine learning	Machine learning
سنة النشأة	2019–2020	2019
صاحب الطريقة	Multiple (e.g., Garg et al.; Yue et al.)	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI Language)
النوع	Domain adaptation for extractive/generative QA	Pre-trained language model with fine-tuning
المصدر التأسيسي	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.
الأسماء البديلة	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
ذات صلة	6	4
الملخص	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.