  As a promising paradigm, interactive semantic parsing has shown to improve
both semantic parsing accuracy and user confidence in the results. In this
paper, we propose a new, unified formulation of the interactive semantic
parsing problem, where the goal is to design a model-based intelligent agent.
The agent maintains its own state as the current predicted semantic parse,
decides whether and where human intervention is needed, and generates a
clarification question in natural language. A key part of the agent is a world
model: it takes a percept (either an initial question or subsequent feedback
from the user) and transitions to a new state. We then propose a simple yet
remarkably effective instantiation of our framework, demonstrated on two
text-to-SQL datasets (WikiSQL and Spider) with different state-of-the-art base
semantic parsers. Compared to an existing interactive semantic parsing approach
that treats the base parser as a black box, our approach solicits less user
feedback but yields higher run-time accuracy.
