  Adversarial attack serves as a major challenge for neural network models in
NLP, which precludes the model's deployment in safety-critical applications. A
recent line of work, detection-based defense, aims to distinguish adversarial
sentences from benign ones. However, {the core limitation of previous detection
methods is being incapable of giving correct predictions on adversarial
sentences unlike defense methods from other paradigms.} To solve this issue,
this paper proposes TextShield: (1) we discover a link between text attack and
saliency information, and then we propose a saliency-based detector, which can
effectively detect whether an input sentence is adversarial or not. (2) We
design a saliency-based corrector, which converts the detected adversary
sentences to benign ones. By combining the saliency-based detector and
corrector, TextShield extends the detection-only paradigm to a
detection-correction paradigm, thus filling the gap in the existing
detection-based defense. Comprehensive experiments show that (a) TextShield
consistently achieves higher or comparable performance than state-of-the-art
defense methods across various attacks on different benchmarks. (b) our
saliency-based detector outperforms existing detectors for detecting
adversarial sentences.
