  Recent studies have shown that deep neural networks are vulnerable to
intentionally crafted adversarial examples, and various methods have been
proposed to defend against adversarial word-substitution attacks for neural NLP
models. However, there is a lack of systematic study on comparing different
defense approaches under the same attacking setting. In this paper, we seek to
fill the gap of systematic studies through comprehensive researches on
understanding the behavior of neural text classifiers trained by various
defense methods under representative adversarial attacks. In addition, we
propose an effective method to further improve the robustness of neural text
classifiers against such attacks and achieved the highest accuracy on both
clean and adversarial examples on AGNEWS and IMDB datasets by a significant
margin.
