Machine learningDeep learning / NLP / CV
基于句子嵌入的迁移学习
基于句子嵌入的迁移学习采用大型预训练编码器——例如Sentence-BERT或Universal Sentence Encoder——该编码器已将通用语言知识编码为固定长度的向量，并用少量额外的标注数据将其适配到新任务或新领域。预训练的表示提供了先发优势，通常优于在适度语料库上从头开始训练的特定任务模型。
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来源
- 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. link ↗
- Conneau, A., Kiela, D., Schwentz, H., Barrault, L. & Bordes, A. (2017). Supervised Learning of Universal Sentence Representations from Natural Language Inference Data. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP), 670–680. link ↗
如何引用本页
ScholarGate. (2026, June 3). Transfer Learning with Pre-trained Sentence Embedding Models. ScholarGate. https://scholargate.app/zh/deep-learning/transfer-learning-with-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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