In particular, Artetxe et al. (2018b) show that the adversarial methods of <cite>Conneau et al. (2018)</cite> and Zhang et al. (2017a,b) fail for many language pairs. In this paper, we revisit adversarial training and propose a number of key improvements that yield more robust training and improved mappings. Our main idea is to learn the cross-lingual mapping in a projected latent space and add more constraints to guide the unsupervised mapping in this space.