  Achieving true human-like ability to conduct a conversation remains an
elusive goal for open-ended dialogue systems. We posit this is because extant
approaches towards natural language generation (NLG) are typically construed as
end-to-end architectures that do not adequately model human generation
processes. To investigate, we decouple generation into two separate phases:
planning and realization. In the planning phase, we train two planners to
generate plans for response utterances. The realization phase uses response
plans to produce an appropriate response. Through rigorous evaluations, both
automated and human, we demonstrate that decoupling the process into planning
and realization performs better than an end-to-end approach.
