Reviews: Style Transfer from Non-Parallel Text by Cross-Alignment

This paper presents a method for learning style transfer models based on non-parallel corpora. The premise of the work is that it is possible to disentangle the style from the content and that when there are two different corpora on the same content but in distinctly different styles, then it is possible to induce the content and the style components. While part of me is somewhat skeptical whether it is truly possible to separate out the style from the content of natural language text, and that I tend to think sentiment and word-reordering presented in this work as applications correspond more to the content of an article than the style, I do believe that this paper presents a very creative and interesting exploration that makes both theoretical and empirical contributions. I imagine ConvNets make stronger discriminators, thus it'd be helpful if the paper can shed lights on how much the quality of the discriminators influence the overall performance of the generators. For example, what kind of RNNs are used for the encoder and the generator?