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Brian Howard

2 accepted papers

2021

Unsupervised Data Augmentation with Naive Augmentation and without Unlabeled Data

EMNLP 2021main

Unsupervised Data Augmentation (UDA) is a semisupervised technique that applies a consistency loss to penalize differences between a model’s predictions on (a) observed (unlabeled) examples; and (b) corresponding ‘noised’ examples produced via data augmentation. While UDA has gained popularity for t…

Cited by 27SourcePDFScholar