We train these models using EM for 500 iterations or until convergence, and we select the model with the lowest perplexity from among 70 random restarts.
here we note a mixed result: whilst de, sv, and it do benefit from POS information, the other languages do not, obtaining great improvements from MUSE embed-dings instead.