Text Transformations in Contrastive Self-Supervised Learning: A Review
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Contrastive self-supervised learning has become a prominent technique in representation learning. The main step in these methods is to contrast semantically similar and dissimilar pairs of samples. However, in the domain of Natural Language Processing (NLP), the augmentation methods used in creating similar pairs with regard to contrastive learning (CL) assumptions are challenging. This is because, even simply modifying a word in the input might change the semantic meaning of the sentence, and hence, would violate the distributional hypothesis. In this review paper, we formalize the contrastive learning framework, emphasize the considerations that need to be addressed in the data transformation step, and review the state-of-the-art methods and evaluations for contrastive representation learning in NLP. Finally, we describe some challenges and potential directions for learning better text representations using contrastive methods.
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The goal of contrastive learning is to bring semantically related samples closer together while keeping dissimilar samples separate.
“The goal of contrastive learning is to bring semantically related samples closer together while keeping dissimilar samples separate [32].”
From Self-supervised learning: A succinct review (2023) · cites this paper as [32]
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- Authors
- Bhattacharjee, A., Karami, M., & Liu, H.
- Affiliations
- Arizona State University (US)
- Venue
- Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
- Published
- 2022-07
- Pages
- 5394-5401
- OpenAlex
- W4221163898
- Cited by
- 20
- References
- 56
- Citations by year
- 2026: 2 · 2024: 5 · 2023: 10 · 2022: 3
- FWCI
- 2.49
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Joint Conference on Artificial Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- U.S. Department of Defense
- Defense Advanced Research Projects Agency (HR001120C0123)
- Office of Naval Research (N00014-21-1-4002, N00014)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Keywords
- self-supervised contrastive learning, text representation learning, data augmentation, textual transformation, distributional hypothesis, natural language processing, semantic similarity
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