BlendCSE: blend contrastive learnings for sentence embeddings with rich semantics and transferability

• Main Authors: Xu, Jiahao, Soh, Charlie Zhanyi, Xu, Liwen, Chen, Lihui
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2024
• Subjects: Engineering::Electrical and electronic engineering; Semantic Sentence Embedding; Contrastive Learning
• Online Access: https://hdl.handle.net/10356/173052
• Institution: Nanyang Technological University

Sentence representation is one of the most fundamental research topics in natural language processing (NLP), as its quality directly affects various downstream task performances. Recent studies for sentence representations have established state-of-the-art (SOTA) performance on semantic representati...