We induced representations of d = 40 dimensions for input vocabularies of |V en in | = 43, 614 and |V de in | = 50, 110 words (filtering out words which occur fewer than five times in our dataset).
to speed up training, 5 we learn on a subset of training sequences choosing the 3,000 most frequent words in en and de for their output vocabularies V en out and V de out , respectively.