方法证据记录
Zero-Shot Classification
Zero-shot classification is a natural-language-processing task that assigns text to categories described in plain language without requiring any labelled training data. Formalised as an entailment problem by Yin, Hay and Roth (2019), it lets a large pretrained language model recognise new categories on the fly simply by naming them, enabling rapid adaptation to fresh label sets.
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。
Zero-Shot Text Classification
分类方法记录 · process-pipeline / text-mining
- Yin, W., Hay, J. & Roth, D. (2019). Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach. EMNLP, 3914-3923. · DOI 10.18653/v1/D19-1404
- Brown, T. et al. (2020). Language Models are Few-Shot Learners. NeurIPS. · URL
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