قارن الطرق

	الإجابة على الأسئلة المتكيفة مع المجال	إجابة الأسئلة المُحسَّنة بدقة
المجال	التعلم العميق	التعلم العميق
العائلة	Machine learning	Machine learning
سنة النشأة	2019–2020	2016–2019
صاحب الطريقة	Multiple (e.g., Garg et al.; Yue et al.)	Devlin et al. (BERT); Rajpurkar et al. (SQuAD benchmark)
النوع	Domain adaptation for extractive/generative QA	Transfer learning / fine-tuning for extractive or generative QA
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. Proceedings of NAACL-HLT 2019, 4171–4186.
الأسماء البديلة	DA-QA, domain-adapted QA, domain-specific question answering, cross-domain question answering	fine-tuned QA, neural QA with fine-tuning, extractive QA fine-tuning, reading comprehension fine-tuning
ذات صلة	6	5
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.	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.