Neural language models for tracking semantic changes over time typically distinguish between two different training protocols-continuous training of models<cite> (Kim et al., 2014)</cite> where the model for each time span is initialized with the embeddings of its predecessor, and, alternatively, independent training with a mapping between models for different points in time (Kulkarni et al., 2015) . A comparison between these two protocols, such as the one proposed in this paper, has not been carried out before. Also, the application of such protocols to non-English corpora is lacking, with the exception of our own work relating to German data (Hellrich and Hahn, 2016b; Hellrich and Hahn, 2016a) .