  We propose a new general training technique for attention mechanisms based on
virtual adversarial training (VAT). VAT can compute adversarial perturbations
from unlabeled data in a semi-supervised setting for the attention mechanisms
that have been reported in previous studies to be vulnerable to perturbations.
Empirical experiments reveal that our technique (1) provides significantly
better prediction performance compared to not only conventional adversarial
training-based techniques but also VAT-based techniques in a semi-supervised
setting, (2) demonstrates a stronger correlation with the word importance and
better agreement with evidence provided by humans, and (3) gains in performance
with increasing amounts of unlabeled data.
