As human annotation for such perturbations is expensive, an automated approach (Alzantot et al., 2018) was studied for English, to generate semanticpreserving adversaries that fool well-trained sentiment analysis and NLI models with success rates of 97% and 70%, respectively.
compared to the best performing MTL baseline, cASE achieves an improvement of 1.7% and 9.5% in Bulgarian (bg) and Urdu (ur) languages respectively.