Stopword Modification

Introduction Over the last few years, a growing interest has been in testing the adversarial robustness of natural language processing (NLP) models. The research in this area covers new techniques for generating adversarial examples and defending against them. Comparing these attacks directly is challenging because they are evaluated on different data and victim models. Replicating earlier work as a baseline takes time and increases the risk of errors because of missing source code.