Constraining the number of training examples is effective for part-of-speech, suggesting that learning each linguistic task requires fewer samples than our control task.
(2017) defined completely random tasks related to Rademacher complexity (Bartlett and Mendelson, 2001) to understand the capacity of neural networks to overfit, showing that they are expressive enough to fit random noise, but still function as effective models.