Conversational interfaces provide a flexible and easy way for users to seek information that may otherwise be difficult or inconvenient to obtain. However, existing interfaces generally fall into one of two categories: FAQs, where users must have a concrete question in order to retrieve a general answer, or dialogs, where users must follow a predefined path but may receive a personalized answer. In this paper, we introduce Conversational Tree Search (CTS) as a new task that bridges the gap between FAQ-style information retrieval and task-oriented dialog, allowing domain-experts to define dialog trees which can then be converted to an efficient dialog policy that learns only to ask the questions necessary to navigate a user to their goal. We collect a dataset for the travel reimbursement domain and demonstrate a baseline as well as a novel deep Reinforcement Learning architecture for this task. Our results show that the new architecture combines the positive aspects of both the FAQ and dialog system used in the baseline and achieves higher goal completion while skipping unnecessary questions.
翻译:对话界面为用户提供了一种灵活且方便的方式获取信息,这些信息可能在其他情况下很难或不方便获得。然而,现有的界面通常分为两类:常见问题解答(FAQ),在其中用户必须有具体问题以获取通用答案;或是对话,其中用户必须按照预定义的路径进行,但可以收到个性化的答案。在本文中,我们将Conversational Tree Search(CTS)介绍为一种新任务,它弥合了常见问题解答风格的信息检索和任务导向的对话之间的差距,允许领域专家定义对话树,然后将其转换为有效的对话策略,仅学习必要的问题以导航用户到其目标。我们为旅行报销领域收集了数据集,并展示了一种基线和一种新型深度强化学习架构来处理此任务。我们的结果显示,该新架构将基线中使用的FAQ和对话系统的积极方面相结合,实现了更高的目标达成率,同时跳过了不必要的问题。