השוואת שיטות

	מענה לשאלות מותאם-תחום	סיווג מבוסס RoBERTa
תחום	למידה עמוקה	למידה עמוקה
משפחה	Machine learning	Machine learning
שנת המקור	2019–2020	2019
הוגה השיטה	Multiple (e.g., Garg et al.; Yue et al.)	Liu, Y. et al. (Facebook AI Research / University of Washington)
סוג	Domain adaptation for extractive/generative QA	Pre-trained transformer fine-tuned for sequence classification
מקור מכונן	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.	Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv preprint arXiv:1907.11692.
כינויים	DA-QA, domain-adapted QA, domain-specific question answering, cross-domain question answering	RoBERTa classifier, RoBERTa text classification, Robustly Optimized BERT Classification, RoBERTa fine-tuning for classification
קשורות	6	5
תקציר	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.	RoBERTa-based Classification applies the RoBERTa pre-trained transformer — trained more robustly than BERT with dynamic masking and larger batches — to text categorisation tasks by adding a lightweight classification head on top of the [CLS] token representation and fine-tuning the entire model on labelled examples. It consistently matches or outperforms BERT on standard NLP benchmarks.