Salīdzināt metodes

	Semi-supervised Question Answering	Jautājumu atbildēšanas sistēmas ar smalku regulēšanu
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2006–2020	2016–2019
Autors	Multiple (Chapelle et al.; Zhu; Clark et al. for NLP applications)	Devlin et al. (BERT); Rajpurkar et al. (SQuAD benchmark)
Tips	Semi-supervised learning applied to extractive/generative QA	Transfer learning / fine-tuning for extractive or generative QA
Pirmavots	Clark, K., Luong, M.-T., Le, Q. V., & Manning, C. D. (2020). ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators. In Proceedings of ICLR 2020.	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.
Citi nosaukumi	Semi-supervised QA, Self-training for QA, Pseudo-labeled Question Answering, SSL-QA	fine-tuned QA, neural QA with fine-tuning, extractive QA fine-tuning, reading comprehension fine-tuning
Saistītās	6	5
Kopsavilkums	Semi-supervised question answering (QA) trains a model on a small labeled set of question-answer pairs, then generates pseudo-labels on a large unlabeled corpus and retrains iteratively. This self-training loop dramatically increases effective training data without the cost of full manual annotation, achieving strong performance on reading comprehension, open-domain QA, and machine reading tasks.	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.