I got stuck on this paragraph from the academic article "Measuring news sentiment": https://www.sciencedirect.com/science/article/pii/S0304407620303535#tbl3
"As is best practice, we split the labeled dataset into a training set, a development set, and a hold-out test set. The development and test sets have 100 observations each, leaving 600 observations for the training set. (..) hyper-parameter optimization is done through grid search, using cross-validation on the training set to evaluate model performance for each possible set of hyper-parameters. The optimal model is then evaluated against the development set. Finally, after all models have been developed, we test them all against our hold-out test set for final results."
To me, what they describe (the bold parts) is incoherent. I would like to know if I'm wrong or not. I understand "cross-validation on the training set" as doing the validation on a subsample of the training set, e.g. doing k-fold cross validation. But if you split the dataset into training, development, and test set, why would you do validation within the training set, as I understand the authors assert (second bold part)? The validation should be done on the development set, shouldn't it? Is that a semantic mistake or are they doing something I don't understand?