To illustrate the limitations of ADAN-GRL and motivate the formal introduction of our ADAN model, consider the distribution of the joint hidden features F for both SOURCE and TARGET instances: In order to learn language-invariant features, ADAN trains F to make these two distributions as close as possible for better cross-lingual for qiter = 1 to k do 4: feature vectors   8: Update Q parameters to minimize loss q ClipW eights(Q, −c, c) 11: Main iteration 12: Sample unlabeled batch x tgt ∼ X tgt 14: 15: 16: Eqn 417: Update F, P parameters to minimize loss 18: until convergence generalization.
as argued by Arjovsky et al.