  Task-oriented dialog systems have been applied in various tasks, such as
automated personal assistants, customer service providers and tutors. These
systems work well when users have clear and explicit intentions that are
well-aligned to the systems' capabilities. However, they fail if users
intentions are not explicit. To address this shortcoming, we propose a
framework to interleave non-task content (i.e. everyday social conversation)
into task conversations. When the task content fails, the system can still keep
the user engaged with the non-task content. We trained a policy using
reinforcement learning algorithms to promote long-turn conversation coherence
and consistency, so that the system can have smooth transitions between task
and non-task content. To test the effectiveness of the proposed framework, we
developed a movie promotion dialog system. Experiments with human users
indicate that a system that interleaves social and task content achieves a
better task success rate and is also rated as more engaging compared to a pure
task-oriented system.
