Since historical spelling normalization is typically a low-resource task, systems should also ideally be tested with varying amounts of training data to assess how much annotation might be required for a new corpus <cite>(Pettersson et al., 2014</cite>; Bollmann and Søgaard, 2016; Korchagina, 2017) .