# Interactive Semantic Parsing for If-Then Recipes via Hierarchical Reinforcement Learning

arXiv: https://arxiv.org/abs/1808.06740 · HTML: https://mlsanity.com/paper/1808.06740/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2018.

## Built on by (most cited first, 7 of 7)

- [A Survey of Human-in-the-loop for Machine Learning](https://mlsanity.com/paper/2108.00941/) (2021)
- [Learning to Simulate Natural Language Feedback for Interactive Semantic Parsing](https://mlsanity.com/paper/2305.08195/) (2023)
- [Towards Transparent Interactive Semantic Parsing via Step-by-Step Correction](https://mlsanity.com/paper/2110.08345/) (2021)
- [Nano: Nested Human-in-the-Loop Reward Learning for Few-shot Language Model Control](https://mlsanity.com/paper/2211.05750/) (2022)
- [What If: Generating Code to Answer Simulation Questions](https://mlsanity.com/paper/2204.07835/) (2022)
- [Error-Aware Interactive Semantic Parsing of OpenStreetMap](https://mlsanity.com/paper/2106.11739/) (2021)
- [Proactive Detection of Physical Inter-rule Vulnerabilities in IoT Services Using a Deep Learning Approach](https://mlsanity.com/paper/2406.03836/) (2024)

## Abstract (arXiv)

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.