Reducing errors among function words that are observed in the parallel data may help prevent the decipherment model from aligning words that are spuriously similar "false friends" when analyzing the monolingual data.
our model is also very scalable, and can be applied to settings with more data than we experiment with here but still insufficient data for modern embedding-based unsupervised methods.