Investigating Reinforcement Learning for Communication Strategies in a Task-Initiative Setting
This work addresses interactive communication strategies for conversational AI systems, presenting an incremental improvement with specific gains in efficiency and transparency.
The paper tackled the problem of optimizing communication strategies in task-initiative conversational systems by analyzing trade-offs between initial presentation and follow-up using reinforcement learning, finding that coherence-based representations offer minimal data needs, explainability, and strong audit capabilities with little loss in predicted outcomes across user models.
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.