(2013b) propose to merge them into a single token in a pre-processing step.
for that purpose, they use a scoring function based on their co-occurence frequency in the training corpus, with a discounting coefficient δ that penalizes rare words, and iteratively merge those above a threshold: we also need to learn representations for compositional n-grams in our scenario, as there is not always a 1:1 correspondence for n-grams across languages even for compositional phrases.