Порівняння методів

	Слабокеровані векторні представлення речень	Напівкеровані ембединги речень
Галузь	Глибоке навчання	Глибоке навчання
Родина	Machine learning	Machine learning
Рік появи	2016–2019	2019–2021
Автор методу	Ratner et al. (weak supervision framework); Reimers & Gurevych (sentence embeddings)	Gao, T.; Reimers, N. et al. (multiple contributors)
Тип	Representation learning under weak supervision	Semi-supervised representation learning
Основоположне джерело	Ratner, A., De Sa, C., Wu, S., Selsam, D., & Re, C. (2016). Data Programming: Creating Large Training Sets, Quickly. Advances in Neural Information Processing Systems (NeurIPS), 29.	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.
Інші назви	WS sentence embeddings, noisy-label sentence representation learning, weakly supervised sentence representation, distant-supervision sentence embeddings	Semi-supervised SimCSE, Self-training sentence encoders, Pseudo-labeled sentence representation learning, SSL sentence embeddings
Пов'язані	6	5
Підсумок	Weakly supervised sentence embeddings train dense sentence representations using noisy, heuristic, or programmatically generated labels instead of costly human annotation. Labeling functions — rules, distant supervision signals, or lightweight classifiers — supply approximate supervision that a label model aggregates into probabilistic labels, which then guide the sentence encoder to produce task-useful representations at scale.	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.