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19 October 2022 A survey of contrastive learning in NLP
Haolin Sun, Jie Liu, Jing Zhang
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Proceedings Volume 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering; 122944C (2022) https://doi.org/10.1117/12.2639685
Event: 7th International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2022), 2022, Xishuangbanna, China
Abstract
Contrastive learning (CL) is building example pairs and computing loss to make models more robust in processing samples by reducing the distance between positive samples and amplifying the distance between negative samples. Recently, CL has attracted interest in natural language processing (NLP) where it has worked well and is mainly applied to sentence embedding and text classification tasks. To our knowledge, no study has reviewed the application of CL in NLP. In this paper, we describe two types of contexts with CL and present the methods to compute different losses. And then, we introduce some classic models which are significant. Finally, we discuss the current challenges and possible future directions of the CL.
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Haolin Sun, Jie Liu, and Jing Zhang "A survey of contrastive learning in NLP", Proc. SPIE 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering, 122944C (19 October 2022); https://doi.org/10.1117/12.2639685
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KEYWORDS
Data modeling
Statistical modeling
Machine learning
Computer programming
Statistical analysis
Data processing
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