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| 약지도 트랜스포머× | 약한 지도 BERT 기반 분류× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2017–2019 | 2017–2020 |
| 창시자≠ | Multiple contributors (weak supervision paradigm: Zhou 2018; transformer backbone: Vaswani et al. 2017) | Multiple (Ratner et al. for weak supervision framework; Meng et al. for BERT integration) |
| 유형≠ | Weakly supervised deep learning | Weakly supervised fine-tuning of pre-trained language model |
| 원전≠ | Ratner, A., Bach, S. H., Ehrenberg, H., Fries, J., Wu, S., & Re, C. (2017). Snorkel: Rapid training data creation with weak supervision. Proceedings of the VLDB Endowment, 11(3), 269–282. DOI ↗ | Meng, Y., Zhang, Y., Huang, J., Xiong, C., Ji, H., Zhang, C., & Han, J. (2020). Text Classification Using Label Names Only: A Language Model Self-Training Approach. Proceedings of EMNLP 2020, 9006–9017. link ↗ |
| 별칭 | WST, weakly supervised attention model, noisy-label transformer, weak supervision with transformers | WS-BERT, BERT with weak supervision, label-efficient BERT classification, noisy-label BERT fine-tuning |
| 관련≠ | 5 | 6 |
| 요약≠ | Weakly Supervised Transformer combines the representational power of Transformer architectures with weak supervision strategies that exploit noisy, incomplete, or programmatically generated labels — making it possible to train high-quality NLP and vision models when fully annotated datasets are scarce or prohibitively expensive to produce. | Weakly supervised BERT-based classification adapts BERT to text classification tasks when only noisy, heuristic, or programmatically generated labels are available instead of clean human annotations. It combines weak supervision frameworks — such as labeling functions and data programming — with BERT's pre-trained language representations to achieve robust classification without expensive hand-labeling. |
| ScholarGate데이터셋 ↗ |
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