As a comparison, D-embeddings are trained on multiple prototypes for each word to record relation information, thus their size dramatically explodes with the increasing of the vocabulary.
it is easily computed for D-embeddings that 200,000 words will result in a 10GB model, which is unfeasible to be used in downstream tasks.