  Given a text description, most existing semantic parsers synthesize a program
in one shot. However, it is quite challenging to produce a correct program
solely based on the description, which in reality is often ambiguous or
incomplete. In this paper, we investigate interactive semantic parsing, where
the agent can ask the user clarification questions to resolve ambiguities via a
multi-turn dialogue, on an important type of programs called "If-Then recipes."
We develop a hierarchical reinforcement learning (HRL) based agent that
significantly improves the parsing performance with minimal questions to the
user. Results under both simulation and human evaluation show that our agent
substantially outperforms non-interactive semantic parsers and rule-based
agents.
