As the training data size increases, one would expect the likelihood of observing rare words to decrease, especially in languages with low morphological complexity, along with the significance of representing rare and unseen words <cite>(Cherry et al., 2018)</cite> . Our results support this hypothesis, where decreasing lexical sparsity, either in the form of the training data size, or the morphological complexity of the target language, eliminates the advantage of character-level translation.