  Adversarial training has shown impressive success in learning bilingual
dictionary without any parallel data by mapping monolingual embeddings to a
shared space. However, recent work has shown superior performance for
non-adversarial methods in more challenging language pairs. In this work, we
revisit adversarial autoencoder for unsupervised word translation and propose
two novel extensions to it that yield more stable training and improved
results. Our method includes regularization terms to enforce cycle consistency
and input reconstruction, and puts the target encoders as an adversary against
the corresponding discriminator. Extensive experimentations with European,
non-European and low-resource languages show that our method is more robust and
achieves better performance than recently proposed adversarial and
non-adversarial approaches.
