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| 준지도 학습 문장 임베딩× | 준지도 학습 트랜스포머× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2019–2021 | 2018–2019 |
| 창시자≠ | Gao, T.; Reimers, N. et al. (multiple contributors) | Devlin, J. et al. (BERT); broader SSL-Transformer paradigm community |
| 유형≠ | Semi-supervised representation learning | Semi-supervised deep learning |
| 원전≠ | Gao, T., Yao, X., & Chen, D. (2021). SimCSE: Simple Contrastive Learning of Sentence Embeddings. In Proceedings of EMNLP 2021 (pp. 6894–6910). Association for Computational Linguistics. DOI ↗ | 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 SimCSE, Self-training sentence encoders, Pseudo-labeled sentence representation learning, SSL sentence embeddings | semi-supervised transformer model, SSL transformer, transformer with self-supervised pre-training, semi-supervised attention model |
| 관련 | 5 | 5 |
| 요약≠ | Semi-supervised sentence embeddings combine a small set of labeled sentence pairs with large quantities of unlabeled text to train dense vector representations of sentences. By exploiting abundant unlabeled data through contrastive objectives or pseudo-labeling, these models produce high-quality embeddings for semantic similarity, retrieval, and classification even when annotated data is scarce. | 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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