  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.
