The outfits people wear contain latent fashion concepts capturing styles, seasons, events, and environments. Fashion theorists have proposed that these concepts are shaped by design elements such as color, material, and silhouette. While a dress may be "bohemian" because of its pattern, material, trim, or some combination thereof, it is not always clear how low-level elements translate to high-level styles. In this paper, we use polylingual topic modeling to learn latent fashion concepts jointly in two languages capturing these elements and styles. This latent topic formation enables translation between languages through topic space, exposing the elements of fashion style. The model is trained on a set of more than half a million outfits collected from Polyvore, a popular fashion-based social network. We present novel, data-driven fashion applications that allow users to express their desires in natural language just as they would to a real stylist, and produce tailored item recommendations for their fashion needs.