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

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

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...

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

Internet

https://hdl.handle.net/10356/173052

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