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

• Main Authors: Xu, Jiahao, Soh, Charlie Zhanyi, Xu, Liwen, Chen, Lihui
• 其他作者: School of Electrical and Electronic Engineering
• 格式: Article
• 語言: English
• 出版: 2024
• 主題: Engineering::Electrical and electronic engineering; Semantic Sentence Embedding; Contrastive Learning
• 在線閱讀: https://hdl.handle.net/10356/173052

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...