  Word-level adversarial attacks have shown success in NLP models, drastically
decreasing the performance of transformer-based models in recent years. As a
countermeasure, adversarial defense has been explored, but relatively few
efforts have been made to detect adversarial examples. However, detecting
adversarial examples may be crucial for automated tasks (e.g. review sentiment
analysis) that wish to amass information about a certain population and
additionally be a step towards a robust defense system. To this end, we release
a dataset for four popular attack methods on four datasets and four models to
encourage further research in this field. Along with it, we propose a
competitive baseline based on density estimation that has the highest AUC on 29
out of 30 dataset-attack-model combinations. Source code is available in
https://github.com/anoymous92874838/text-adv-detection.
