Therefore the network can compute the probability of the output condition both on the input sentence x and the style control variable s. Formally, this can be expressed as: and we denote the predicted output sentence of this network by f θ (x, s).
suppose we use x and s to denote the sentence and its style from the dataset D. Because of the absence of the parallel corpora, we can't directly obtain the supervision for the case f θ (x, s) where s = s. we introduce a discriminator network to learn this supervision from the nonparallel copora.