So sánh phương pháp

	Nhúng câu tự giám sát	Nhúng câu bán giám sát
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2019–2021	2019–2021
Người khởi xướng	Gao, T., Yao, X., & Chen, D. (SimCSE); Reimers, N. & Gurevych, I. (Sentence-BERT)	Gao, T.; Reimers, N. et al. (multiple contributors)
Loại	Self-supervised representation learning	Semi-supervised representation learning
Công trình gốc	Gao, T., Yao, X., & Chen, D. (2021). SimCSE: Simple Contrastive Learning of Sentence Embeddings. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP), 6894–6910.	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.
Tên gọi khác	self-supervised sentence representation learning, contrastive sentence embeddings, SimCSE, unsupervised sentence encoders	Semi-supervised SimCSE, Self-training sentence encoders, Pseudo-labeled sentence representation learning, SSL sentence embeddings
Liên quan	5	5
Tóm tắt	Self-supervised sentence embeddings train a neural encoder to map sentences into a dense vector space without requiring manually labeled pairs. By constructing positive examples automatically — for instance by passing the same sentence through dropout twice — and using contrastive objectives, the model learns semantically rich representations that transfer well to similarity, retrieval, and classification tasks.	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.