  In this paper, we introduce Adversarial-and-attention Network (A3Net) for
Machine Reading Comprehension. This model extends existing approaches from two
perspectives. First, adversarial training is applied to several target
variables within the model, rather than only to the inputs or embeddings. We
control the norm of adversarial perturbations according to the norm of original
target variables, so that we can jointly add perturbations to several target
variables during training. As an effective regularization method, adversarial
training improves robustness and generalization of our model. Second, we
propose a multi-layer attention network utilizing three kinds of
high-efficiency attention mechanisms. Multi-layer attention conducts
interaction between question and passage within each layer, which contributes
to reasonable representation and understanding of the model. Combining these
two contributions, we enhance the diversity of dataset and the information
extracting ability of the model at the same time. Meanwhile, we construct A3Net
for the WebQA dataset. Results show that our model outperforms the
state-of-the-art models (improving Fuzzy Score from 73.50% to 77.0%).
