HNCSE: Advancing Sentence Embeddings via Hybrid Contrastive Learning
with Hard Negatives
HNCSE: Advancing Sentence Embeddings via Hybrid Contrastive Learning
with Hard Negatives
Unsupervised sentence representation learning remains a critical challenge in modern natural language processing (NLP) research. Recently, contrastive learning techniques have achieved significant success in addressing this issue by effectively capturing textual semantics. Many such approaches prioritize the optimization using negative samples. In fields such as computer vision, hard negative samples …