4: Train a skip-gram word embedding model on the resulting list of word and POS sequences from both the source and target language We assume the same POS tagset is used for both the source and target language, 2 and learn word embeddings for each word type in both languages into the same syntactic space of nearby POS contexts.
we develop a predictive model of the tags to the left and right of a word, as illustrated in Figure 1 and outlined in Algorithm 1.