In this paper, we thus propose Multinomial Adversarial Networks (henceforth, MANs) for the task of multi-domain text classification.
to standard adversarial networks (Goodfellow et al., 2014), which serve as a tool for minimizing the divergence between two distributions (Nowozin et al., 2016), MANs represent a family of theoretically sound adversarial networks that, in contrast, leverage a multinomial discriminator to directly minimize the divergence among multiple probability distributions.