In this work, we propose a language-adversarial training approach that does not need a highly engineered MT system, and requires orders of magnitude less in terms of the size of a parallel corpus.
we propose an Adversarial Deep Averaging Network (ADAN) that leverages a set of bilingual word embeddings (BWEs; Zou et al., 2013) trained on bitexts, in order to eliminate the need for labeled TARGET training data.