  Active learning for sentence understanding aims at discovering informative
unlabeled data for annotation and therefore reducing the demand for labeled
data. We argue that the typical uncertainty sampling method for active learning
is time-consuming and can hardly work in real-time, which may lead to
ineffective sample selection. We propose adversarial uncertainty sampling in
discrete space (AUSDS) to retrieve informative unlabeled samples more
efficiently. AUSDS maps sentences into latent space generated by the popular
pre-trained language models, and discover informative unlabeled text samples
for annotation via adversarial attack. The proposed approach is extremely
efficient compared with traditional uncertainty sampling with more than 10x
speedup. Experimental results on five datasets show that AUSDS outperforms
strong baselines on effectiveness.
