Subsequent work has proposed alternative optimization objectives to learn better mappings. <cite>Xing et al. (2015)</cite> incorporate length normalization in the training of word embeddings and try to maximize the cosine similarity instead, introducing an orthogonality constraint to preserve the length normalization after the projection.