Recently with the wide-spread of conversational devices, more and more
people started to realize the importance of dialog research. However,
some of them are still living in a simulated world, using simulated
data such as  Facebook bAbI.  In this talk, we emphasize that dialog
research needs to be grounded with the users’ real need.  We
introduce three user-centered task-oriented dialog systems that are trained by
reinforcement learning algorithms. The first system is a dialog systems that
utilized reinforcement learning to interleave social conversation and
task conversation to promote movies more effectively. The second
system is a sentiment adaptive bus information search system. It uses
sentiment as immediate reward to help the end-to-end RL dialog
framework to converge faster and better. The trained dialog policy
will also have a user friendly effect. It would adapt to user’s
sentiment when choosing dialog action templates. For example, the
policy will pick template that provides more detailed instructions
when user is being negative. This is extremely useful for customer
service dialog systems where users frequently get angry. The third
system is a task-oriented visual dialog systems. It uses a
hierarchical reinforcement learning to track multimodal dialog states
and decide among sub tasks of whether to ask more information or just
give an answer. Such system can complete the task more successfully
and effectively. We are conducting a further experiment to deploy the
system as a shopping assistant.