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method
ID: unsuperviseddataaugmentation
Related Papers
Unsupervised Data Augmentation with Naive Augmentation and without Unlabeled Data
AbstractUnsupervised 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 …...
Unsupervised Data Augmentation for Aspect Based Sentiment Analysis
AbstractRecent approaches to Aspect-based Sentiment Analysis (ABSA) take a co-extraction approach to this span-level classification task, performing the subtasks of aspect term extraction (ATE) and aspect sentiment classification (ASC) simultaneously. …...