Machine learningDeep learning / NLP / CV
半监督句子嵌入
半监督句子嵌入结合了少量标记的句子对和大量的无标记文本，以训练句子的密集向量表示。通过利用无标记数据（例如，通过对比目标或伪标记），这些模型即使在标记数据稀缺的情况下，也能为语义相似性、检索和分类生成高质量的嵌入。
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来源
- Gao, T., Yao, X., & Chen, D. (2021). SimCSE: Simple Contrastive Learning of Sentence Embeddings. In Proceedings of EMNLP 2021 (pp. 6894–6910). Association for Computational Linguistics. DOI: 10.18653/v1/2021.emnlp-main.552 ↗
- Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. In Proceedings of EMNLP-IJCNLP 2019 (pp. 3982–3992). Association for Computational Linguistics. DOI: 10.18653/v1/D19-1410 ↗
如何引用本页
ScholarGate. (2026, June 3). Semi-supervised Sentence Embeddings (Contrastive and Self-training Approaches). ScholarGate. https://scholargate.app/zh/deep-learning/semi-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.
- [需翻译标题：BERT-based Classification...]深度学习↔ compare
- 自监督句子嵌入深度学习↔ compare
- 半监督式BERT分类深度学习↔ compare
- 半监督式 Transformer深度学习↔ compare
- 句子嵌入深度学习↔ compare