Porovnat metody

	Kontrastivní učení pro NLP	Sémantická podobnost
Obor	Dolování textu	Dolování textu
Rodina	Process / pipeline	Process / pipeline
Rok vzniku	2020–2021	2019
Tvůrce	Gao, Yao & Chen (SimCSE, 2021); Khosla et al. (Supervised Contrastive, 2020)	Nils Reimers & Iryna Gurevych (Sentence-BERT)
Typ	Self-supervised / supervised representation learning	NLP text-comparison task
Původní zdroj	Gao, T., Yao, X., & Chen, D. (2021). SimCSE: Simple Contrastive Learning of Sentence Embeddings. Proceedings of EMNLP 2021.	Reimers, N. & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. EMNLP.
Další názvy	SimCSE, contrastive sentence embeddings, ContrastiveBERT, Karşıtlık Öğrenmesi — NLP (Contrastive Learning)	semantic textual similarity, text similarity, Anlamsal Benzerlik Analizi
Příbuzné	4	4
Shrnutí	Contrastive learning for NLP is a representation-learning technique — popularised by SimCSE (Gao et al., 2021) and Supervised Contrastive Learning (Khosla et al., 2020) — that trains a text encoder by pulling embeddings of similar text pairs together while pushing embeddings of dissimilar pairs apart. The result is a dense, high-quality embedding space that can be learned with no labels at all, or with minimal supervision, making it especially valuable when annotated data are scarce.	Semantic similarity analysis measures how close in meaning two texts are, rather than how many words they share on the surface. Building on the Sentence-BERT work of Reimers and Gurevych (2019), it represents each text as a vector and compares those vectors so that paraphrases score high even when their wording differs.