  Unsupervised word translation from non-parallel inter-lingual corpora has
attracted much research interest. Very recently, neural network methods trained
with adversarial loss functions achieved high accuracy on this task. Despite
the impressive success of the recent techniques, they suffer from the typical
drawbacks of generative adversarial models: sensitivity to hyper-parameters,
long training time and lack of interpretability. In this paper, we make the
observation that two sufficiently similar distributions can be aligned
correctly with iterative matching methods. We present a novel method that first
aligns the second moment of the word distributions of the two languages and
then iteratively refines the alignment. Extensive experiments on word
translation of European and Non-European languages show that our method
achieves better performance than recent state-of-the-art deep adversarial
approaches and is competitive with the supervised baseline. It is also
efficient, easy to parallelize on CPU and interpretable.
