  Automatic transfer of text between domains has become popular in recent
times. One of its aims is to preserve the semantic content of text being
translated from source to target domain. However, it does not explicitly
maintain other attributes between the source and translated text, for e.g.,
text length and descriptiveness. Maintaining constraints in transfer has
several downstream applications, including data augmentation and de-biasing. We
introduce a method for such constrained unsupervised text style transfer by
introducing two complementary losses to the generative adversarial network
(GAN) family of models. Unlike the competing losses used in GANs, we introduce
cooperative losses where the discriminator and the generator cooperate and
reduce the same loss. The first is a contrastive loss and the second is a
classification loss, aiming to regularize the latent space further and bring
similar sentences across domains closer together. We demonstrate that such
training retains lexical, syntactic, and domain-specific constraints between
domains for multiple benchmark datasets, including ones where more than one
attribute change. We show that the complementary cooperative losses improve
text quality, according to both automated and human evaluation measures.
