Specifically, for each unlabeled source phrase f , we learn a mapping W f ∈ R d×d based on the translations of m of f 's labeled neighbors: Compared to the global projection, we require an additional k-NN query to find the labeled neighbors for each unlabeled source phrase.
this extra computation takes only a negligible amount of time, since the number of labeled phrases on the source side is significantly smaller than the number of phrases on the target side.