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Recent studies have shown that deep neural networks are vulnerable tointentionally crafted adversarial examples, and various methods have beenproposed to defend against adversarial word-substitution attacks for neural NLPmodels. However, there is a lack of systematic study on comparing differentdefense approaches under the same attacking setting. In this paper, we seek tofill the gap of systematic studies through comprehensive researches onunderstanding the behavior of neural text classifiers trained by variousdefense methods under representative adversarial attacks. In addition, wepropose an effective method to further improve the robustness of neural textclassifiers against such attacks and achieved the highest accuracy on bothclean and adversarial examples on AGNEWS and IMDB datasets by a significantmargin.

Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution