Salīdzināt metodes

	Vāji uzraudzīta jautājumu atbildēšana	BERT klasifikācija
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2017–2019	2019
Autors	Multiple authors (Clark, Gardner, Min et al.)	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI Language)
Tips	Weakly supervised NLP model	Pre-trained language model with fine-tuning
Pirmavots	Clark, C., & Gardner, M. (2018). Simple and Effective Multi-Paragraph Reading Comprehension. In Proceedings of ACL 2018, pp. 845–855. Association for Computational Linguistics.	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.
Citi nosaukumi	WS-QA, distantly supervised QA, noisy-label question answering, indirect supervision QA	BERT classifier, BERT fine-tuning for classification, BERT text classification, BERT-CLS
Saistītās	4	4
Kopsavilkums	Weakly supervised question answering (WS-QA) trains neural reading-comprehension models using indirect or automatically derived answer labels rather than expensive human-annotated span annotations. By exploiting distant supervision, heuristic labeling, or answer-presence signals, WS-QA makes QA feasible in domains and languages where full annotation is impractical.	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.