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.

翻译：许多对话系统需要向用户呈现细微复杂的信息。这类系统必须对其陈述内容进行后续跟进，以解答澄清性问题并修复误解。本研究在指称性交流任务中探索了这一交互策略。通过仿真模拟，我们分析了初始信息呈现与后续跟进之间的沟通权衡（该权衡随用户澄清策略变化），并将若干基线策略的表现与强化学习获得的策略进行对比。研究发现，基于连贯性的对话策略表征具有显著优势：不仅所需数据量极小、决策可解释性强、审计能力突出，而且在不同用户模型下预测结果的性能损失微乎其微。