This paper focuses on style transfer on the basis of un-paired text. This is an instance of broader family of problems including machine translation, decipherment, and sentiment modification. The key technical challenge is to separate the content from desired text characteristics such as sentiment. We leverage refined cross-alignment of latent representations, across mono-lingual text corpora with different characteristics. We deliberately modify encoded examples according to their characteristics, requiring the reproduced instances to match, as a population, available examples with the altered characteristics. We demonstrate the effectiveness of the method on three tasks: sentiment modification, decipherment of word substitution cyphers, and recovery of word reodering.