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LAMBADA
It learns a label-conditioned generator by finetuning GPT-2 on the training data, using this to generate candidate examples per class. A classifier trained on the original training set is then used to select top k candidate examples that confidently belong to the respective class for augmentation.
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Data Science
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Related
BackTranslation
Improving Neural Machine Translation Models with Monolingual Data
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Trade-off of Model-Based Data Augmentation Techniques in NLP