  Neural dialog models have exhibited strong performance, however their
end-to-end nature lacks a representation of the explicit structure of dialog.
This results in a loss of generalizability, controllability and a data-hungry
nature. Conversely, more traditional dialog systems do have strong models of
explicit structure. This paper introduces several approaches for explicitly
incorporating structure into neural models of dialog. Structured Fusion
Networks first learn neural dialog modules corresponding to the structured
components of traditional dialog systems and then incorporate these modules in
a higher-level generative model. Structured Fusion Networks obtain strong
results on the MultiWOZ dataset, both with and without reinforcement learning.
Structured Fusion Networks are shown to have several valuable properties,
including better domain generalizability, improved performance in reduced data
scenarios and robustness to divergence during reinforcement learning.
