方法证据记录
Semi-supervised Sentence Embeddings
Semi-supervised sentence embeddings combine a small set of labeled sentence pairs with large quantities of unlabeled text to train dense vector representations of sentences. By exploiting abundant unlabeled data through contrastive objectives or pseudo-labeling, these models produce high-quality embeddings for semantic similarity, retrieval, and classification even when annotated data is scarce.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Semi-supervised Sentence Embeddings (Contrastive and Self-training Approaches)
分类方法记录 · ml-model / deep-learning
- 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
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