We can then conclude that perplexity selection may not be an appropriate way to determine the optimal amount of unlabeled-domain data to use for NMT models.
if computational resources are limited, according to the experiment results ( Figure  2) in our work, we recommend 1024k as the first choice for cutoffs on ranked unlabeled-domain data, for NMT domain adaptation models trained with curriculum learning strategy.