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Active learning for sentence understanding attempts to reduce the annotationcost by identifying the most informative examples. Common methods for activelearning use either uncertainty or diversity sampling in the pool-basedscenario. In this work, to incorporate both predictive uncertainty and samplediversity, we propose Virtual Adversarial Perturbation for Active Learning(VAPAL) , an uncertainty-diversity combination framework, using virtualadversarial perturbation (Miyato et al., 2019) as model uncertaintyrepresentation. VAPAL consistently performs equally well or even better thanthe strong baselines on four sentence understanding datasets: AGNEWS, IMDB,PUBMED, and SST-2, offering a potential option for active learning on sentenceunderstanding tasks.

Uncertainty Sentence Sampling by Virtual Adversarial Perturbation