To model word senses for NMT, we concatenate the embedding of each token with a vector representation of its sense, either obtained from one of the clustering methods presented in §2, or learned during encoding, as we will explain.
the new vector w i representing each source token w i consists of two parts: where w i is the word embedding learned by the NMT, and µ i is the sense embedding obtained from WSD or learned by the NMT.