Machine learningDeep learning / NLP / CV
自监督句子嵌入
自监督句子嵌入训练一个神经编码器，将句子映射到一个稠密的向量空间，而无需手动标记的配对。通过自动构建正例——例如，将同一个句子通过两次dropout——并使用对比目标，模型可以学习到语义丰富的表示，这些表示能够很好地迁移到相似性、检索和分类任务中。
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Method map
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来源
- Gao, T., Yao, X., & Chen, D. (2021). SimCSE: Simple Contrastive Learning of Sentence Embeddings. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP), 6894–6910. DOI: 10.18653/v1/2021.emnlp-main.552 ↗
- Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 3982–3992. DOI: 10.18653/v1/D19-1410 ↗
如何引用本页
ScholarGate. (2026, June 3). Self-supervised Learning for Sentence Embeddings. ScholarGate. https://scholargate.app/zh/deep-learning/self-supervised-sentence-embeddings
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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