Contrastive Learning Models for Sentence Representations

Lingling XU, Haoran XIE*, Zongxi LI, Fu Lee WANG, Weiming WANG, Qing LI

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

5 Citations (Scopus)


Sentence representation learning is a crucial task in natural language processing, as the quality of learned representations directly influences downstream tasks, such as sentence classification and sentiment analysis. Transformer-based pretrained language models such as bidirectional encoder representations from transformers (BERT) have been extensively applied to various natural language processing tasks, and have exhibited moderately good performance. However, the anisotropy of the learned embedding space prevents BERT sentence embeddings from achieving good results in the semantic textual similarity tasks. It has been shown that contrastive learning can alleviate the anisotropy problem and significantly improve sentence representation performance. Therefore, there has been a surge in the development of models that utilize contrastive learning to fine-tune BERT-like pretrained language models to learn sentence representations. But no systematic review of contrastive learning models for sentence representations has been conducted. To fill this gap, this article summarizes and categorizes the contrastive learning based sentence representation models, common evaluation tasks for assessing the quality of learned representations, and future research directions. Furthermore, we select several representative models for exhaustive experiments to illustrate the quantitative improvement of various strategies on sentence representations.

Original languageEnglish
Article number67
Pages (from-to)1-34
Number of pages34
JournalACM Transactions on Intelligent Systems and Technology
Issue number4
Early online date2 May 2023
Publication statusPublished - 15 Jun 2023

Bibliographical note

Funding Information:
This research was supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (UGC/FDS16/E01/19) and the Lam Woo Research Fund (LWP20019) of Lingnan University.

Publisher Copyright:
© 2023 Copyright held by the owner/author(s).


  • Additional Key Words and PhrasesSentence representation learning
  • BERT
  • contrastive learning
  • Data Augmentation


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