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：对话式界面为用户提供了一种灵活且简便的方式来获取通常难以或不方便获得的信息。然而，现有界面通常分为两类：常见问题解答（FAQ），用户必须提出具体问题才能获得一般性答案；或对话系统，用户需遵循预定义路径，但可能获得个性化答案。本文提出对话式树搜索（CTS）作为一种新任务，弥合了FAQ式信息检索与面向任务对话之间的鸿沟，允许领域专家定义对话树，进而将其转化为高效的对话策略，该策略仅学习询问引导用户达成目标所必需的问题。我们为差旅报销领域收集了一个数据集，并针对此任务展示了基线方法及一种新颖的深度强化学习架构。结果表明，新架构融合了基线中FAQ和对话系统的积极特性，在跳过不必要问题的同时实现了更高的目标完成率。