That’s very interesting!
I agree, automatic data augmentation is still something somehow mysterious to me in NLP since it is way less controllable than in vision. It seems fine to me that the resulting examples are extremely noisy (I saw some works in vision where perturbed images where the original label becomes quite ambiguous). There might be a balance to find: you want the model to learn through the noise but also not to be over-confident when you have ambiguous examples…
Do you have guidelines you can share on the data augmentation in NLP? In which case it works? Why it works? Or a survey?