Natural language generators for task-oriented dialog should be able to vary the style of the output while still effectively realizing the system dialog actions and their associated semantics. The use of neural natural language generation (NNLG) for training the response generation component of conversational agents promises to simplify the process of producing high quality responses in new domains by relying on the neural architecture to automatically learn how to map an input meaning representation to an output utterance. However, there has been little investigation of NNLGs for dialog that can vary their response style, and we know of no experiments on models that can generate responses that are different in style from those seen during training, while still maintaining semantic fidelity to the input meaning representation. Instead, work on stylistic transfer has focused on tasks where only coarse-grained semantic fidelity is needed, such as controlling the sentiment of the utterance (positive or negative), or the topic or entity under discussion [1, 2, 3] . Consider for example a training instance for the restaurant domain consisting of a meaning representation (MR) from the End-to-End (E2E) Generation Challenge 1 and a sample output from one of our neural generation models in Figure 1 [4, <cite>5]</cite> .