This is not surprising, as when there is enough in-domain data, continued training on only the in-domain data can already achieve a pretty good performance, and we do not need to use extra unlabeled-domain data to augment it any more, neither does curriculum learning.
the probabilistic curriculum (Bengio et al., 2009) works by dividing the training procedure into distinct phases.