Given a mixed-domain training corpus D = {(x, y, d)}, we train the proposed model accord-ing to the following objective function: where J nmt ( * ), J s dc ( * ), J t dc ( * ) and J s * adc ( * ) are the objective functions of NMT, source-side domain classifier, target-side domain classifier, and source-side adversarial domain classifier, respectively, θ={θ nmt , θ s dc , θ t dc , θ s1 adc , θ s2 adc }, and λ is the hyper-parameter for adversarial learning.
to ensure encoding accuracy of domain-shared contexts, we follow  to adopt an alternative two-phase strategy in training, where we alternatively optimize J (D; θ) with θ s1 adc and {θ-θ s1 adc } respectively fixed at a time.