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| 준지도형 질의응답× | 준지도 학습 트랜스포머× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2006–2020 | 2018–2019 |
| 창시자≠ | Multiple (Chapelle et al.; Zhu; Clark et al. for NLP applications) | Devlin, J. et al. (BERT); broader SSL-Transformer paradigm community |
| 유형≠ | Semi-supervised learning applied to extractive/generative QA | Semi-supervised deep learning |
| 원전≠ | 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. link ↗ | 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. DOI ↗ |
| 별칭 | Semi-supervised QA, Self-training for QA, Pseudo-labeled Question Answering, SSL-QA | semi-supervised transformer model, SSL transformer, transformer with self-supervised pre-training, semi-supervised attention model |
| 관련≠ | 6 | 5 |
| 요약≠ | 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. | Semi-supervised learning with Transformer architectures leverages large quantities of unlabeled data alongside a small labeled set to train powerful sequence models. The dominant pattern — exemplified by BERT — first pre-trains the Transformer on unlabeled data using self-supervised objectives such as masked token prediction, then fine-tunes it on the labeled task. This two-stage approach dramatically reduces the labeled data needed to achieve strong performance. |
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