  Natural language generators for task-oriented dialog should be able to vary
the style of the output utterance while still effectively realizing the system
dialog actions and their associated semantics. While the use of neural
generation for training the response generation component of conversational
agents promises to simplify the process of producing high quality responses in
new domains, to our knowledge, there has been very little investigation of
neural generators for task-oriented 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 maintain- ing
semantic fidelity to the input meaning representation. Here, we show that a
model that is trained to achieve a single stylis- tic personality target can
produce outputs that combine stylistic targets. We carefully evaluate the
multivoice outputs for both semantic fidelity and for similarities to and
differences from the linguistic features that characterize the original
training style. We show that contrary to our predictions, the learned models do
not always simply interpolate model parameters, but rather produce styles that
are distinct, and novel from the personalities they were trained on.
