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Contrastive Learning for NLP

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.

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Contrastive Learning for Natural Language Processing

Taxonomický záznam metódy · process-pipeline / text-mining

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Same method familyBERT Embeddingsmachine-suggested · Relational suggestion, not evidence.See alsoSelf-supervised Learningmachine-suggested · Relational suggestion, not evidence.Same method familySemantic Similaritymachine-suggested · Relational suggestion, not evidence.Same method familyText Classificationmachine-suggested · Relational suggestion, not evidence.