In Neural Networks and deep neural networks what does label-dropout mean
I ended up finding the paper you were referencing, maybe next time, add it to your post.

From what I can understand in this paper, label-dropout means that you are dropping the real labels and replace them with others.

The section of the paper you're referring to is explaining everything in great detail, so I'd try to read it carefully. In short, instead of using the ground-truth distribution (i.e. one 1 and a bunch of 0s) in the target layer, they are adding some uncertainty. That way, the model becomes less confident, and therefore is less prone to overfitting and becomes more adaptable.