- The main weaknesses of the paper are the experiments, which is understandable for a short paper but I'd still expect it to be stronger. First, the setting is only on extremely low-resource regime, which is not the only case we want to use data augmentation in real-world applications. Also, sentence classification is an easier task. I feel like the proposed augmentation method has potential to be used on more NLP tasks, which was unfortunately not shown.
- The proposed mixup strategy is very simple (Equation 5), I wonder if the authors have tried other ways to interpolate the one-hot vector with the MLM smoothed vector.
- How does \lambda influence the performances?
- How does the augmentation method compare to other baselines with more training data?
- The main weaknesses of the paper are the experiments, which is understandable for a short paper but I'd still expect it to be stronger. First, the setting is only on extremely low-resource regime, which is not the only case we want to use data augmentation in real-world applications. Also, sentence classification is an easier task. I feel like the proposed augmentation method has potential to be used on more NLP tasks, which was unfortunately not shown.