Salīdzināt metodes

	Jautājumu atbildēšanas sistēmas ar smalku regulēšanu	Klasifikācija, kas pielāgota ar BERT
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2016–2019	2019
Autors	Devlin et al. (BERT); Rajpurkar et al. (SQuAD benchmark)	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI)
Tips	Transfer learning / fine-tuning for extractive or generative QA	Pre-trained transformer fine-tuned for classification
Pirmavots	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.	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	fine-tuned QA, neural QA with fine-tuning, extractive QA fine-tuning, reading comprehension fine-tuning	BERT fine-tuning, BERT classifier, fine-tuned BERT, BERT sequence classification
Saistītās	5	5
Kopsavilkums	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.	Fine-Tuned BERT-based Classification adapts a pre-trained BERT transformer to a specific text classification task by adding a lightweight output layer and continuing gradient-based training on labelled examples. It consistently achieves near-state-of-the-art accuracy on sentiment analysis, topic categorisation, intent detection, and other NLP classification tasks with relatively small labelled datasets.