方法证据记录
Semi-supervised BERT-based Classification
Semi-supervised BERT-based classification fine-tunes a pre-trained BERT encoder on a small pool of labeled text examples while simultaneously leveraging a much larger body of unlabeled text — via consistency training, pseudo-labeling, or data augmentation — to produce high-quality classifiers even when manual annotation 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 BERT-based Text Classification
分类方法记录 · ml-model / deep-learning
- Xie, Q., Dai, Z., Hovy, E., Luong, T., & Le, Q. (2020). Unsupervised Data Augmentation for Consistency Training. Advances in Neural Information Processing Systems (NeurIPS), 33, 27780–27792. · URL
- Chen, J., Yang, Z., & Yang, D. (2020). MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), 2147–2157. · DOI 10.18653/v1/2020.acl-main.194
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