论文标题
当前以任务为导向的对话模型可以在野外自动化现实世界的场景？
Can Current Task-oriented Dialogue Models Automate Real-world Scenarios in the Wild?
论文作者
论文摘要
面向任务的对话（TOD）系统主要基于基于插槽的TOD（SF-TOD）框架，其中对话被分解为较小，可控制的单元（即插槽）以完成特定任务。基于此框架的一系列方法在各种TOD基准上取得了巨大的成功。但是，我们认为当前的TOD基准仅限于替代现实世界的场景，并且当前的TOD模型仍然是涵盖这些方案的很长的路要走。在该职位论文中，我们首先确定SF-TOD系统的当前状态和局限性。之后，我们探索WebTOD框架，这是当可用Web/Mobile接口时构建可扩展TOD系统的替代方向。在WebTOD中，对话系统学习了如何理解人类代理与大规模语言模型供电的网络/移动接口相互作用。
Task-oriented dialogue (TOD) systems are mainly based on the slot-filling-based TOD (SF-TOD) framework, in which dialogues are broken down into smaller, controllable units (i.e., slots) to fulfill a specific task. A series of approaches based on this framework achieved remarkable success on various TOD benchmarks. However, we argue that the current TOD benchmarks are limited to surrogate real-world scenarios and that the current TOD models are still a long way to cover the scenarios. In this position paper, we first identify current status and limitations of SF-TOD systems. After that, we explore the WebTOD framework, the alternative direction for building a scalable TOD system when a web/mobile interface is available. In WebTOD, the dialogue system learns how to understand the web/mobile interface that the human agent interacts with, powered by a large-scale language model.
