  Complex conversation settings such as persuasion involve communicating
changes in attitude or behavior, so users' perspectives need to be addressed,
even when not directly related to the topic. In this work, we contribute a
novel modular dialogue system framework that seamlessly integrates factual
information and social content into persuasive dialogue. Our framework is
generalizable to any dialogue tasks that have mixed social and task contents.
We conducted a study that compared user evaluations of our framework versus a
baseline end-to-end generation model. We found our framework was evaluated more
favorably in all dimensions including competence and friendliness, compared to
the end-to-end model which does not explicitly handle social content or factual
questions.
