  In the area of natural language processing, deep learning models are recently
known to be vulnerable to various types of adversarial perturbations, but
relatively few works are done on the defense side. Especially, there exists few
effective defense method against the successful synonym substitution based
attacks that preserve the syntactic structure and semantic information of the
original text while fooling the deep learning models. We contribute in this
direction and propose a novel adversarial defense method called Synonym
Encoding Method (SEM). Specifically, SEM inserts an encoder before the input
layer of the target model to map each cluster of synonyms to a unique encoding
and trains the model to eliminate possible adversarial perturbations without
modifying the network architecture or adding extra data. Extensive experiments
demonstrate that SEM can effectively defend the current synonym substitution
based attacks and block the transferability of adversarial examples. SEM is
also easy and efficient to scale to large models and big datasets.
