  This paper presents VirAAL, an Active Learning framework based on Adversarial
Training. VirAAL aims to reduce the effort of annotation in Natural Language
Understanding (NLU). VirAAL is based on Virtual Adversarial Training (VAT), a
semi-supervised approach that regularizes the model through Local
Distributional Smoothness. With that, adversarial perturbations are added to
the inputs making the posterior distribution more consistent. Therefore,
entropy-based Active Learning becomes robust by querying more informative
samples without requiring additional components. The first set of experiments
studies the impact of an adapted VAT for joint-NLU tasks within low labeled
data regimes. The second set shows the effect of VirAAL in an Active Learning
(AL) process. Results demonstrate that VAT is robust even on multi-task
training, where the adversarial noise is computed from multiple loss functions.
Substantial improvements are observed with entropy-based AL with VirAAL for
querying data to annotate. VirAAL is an inexpensive method in terms of AL
computation with a positive impact on data sampling. Furthermore, VirAAL
decreases annotations in AL up to 80% and shows improvements over existing data
augmentation methods. The code is publicly available.
