Investigating Reinforcement Learning for Communication Strategies in a
  Task-Initiative Setting
Baber Khalid Matthew Stone
Many conversational domains require the system to present nuanced information
to users. Such systems must follow up what they say to address clarification
questions and repair misunderstandings. In this work, we explore this
interactive strategy in a referential communication task. Using simulation, we
analyze the communication trade-offs between initial presentation and
subsequent followup as a function of user clarification strategy, and compare
the performance of several baseline strategies to policies derived by
reinforcement learning. We find surprising advantages to coherence-based
representations of dialogue strategy, which bring minimal data requirements,
explainable choices, and strong audit capabilities, but incur little loss in
predicted outcomes across a wide range of user models.