# Genie: A Generator of Natural Language Semantic Parsers for Virtual Assistant Commands

arXiv: https://arxiv.org/abs/1904.09020 · HTML: https://mlsanity.com/paper/1904.09020/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2019.

## Built on by (most cited first, 19 of 19)

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- [Factoring Statutory Reasoning as Language Understanding Challenges](https://mlsanity.com/paper/2105.07903/) (2021)
- [GraphQ IR: Unifying the Semantic Parsing of Graph Query Languages with One Intermediate Representation](https://mlsanity.com/paper/2205.12078/) (2022)
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- [Contextual Semantic Parsing for Multilingual Task-Oriented Dialogues](https://mlsanity.com/paper/2111.02574/) (2021)
- [Structurally Diverse Sampling for Sample-Efficient Training and Comprehensive Evaluation](https://mlsanity.com/paper/2203.08445/) (2022)
- [ThingTalk: An Extensible, Executable Representation Language for Task-Oriented Dialogues](https://mlsanity.com/paper/2203.12751/) (2022)
- [DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues](https://mlsanity.com/paper/2506.22853/) (2025)
- [Zero and Few-Shot Localization of Task-Oriented Dialogue Agents with a Distilled Representation](https://mlsanity.com/paper/2302.09424/) (2023)
- [ReactGenie: A Development Framework for Complex Multimodal Interactions Using Large Language Models](https://mlsanity.com/paper/2306.09649/) (2023)
- [Fine-tuned LLMs Know More, Hallucinate Less with Few-Shot Sequence-to-Sequence Semantic Parsing over Wikidata](https://mlsanity.com/paper/2305.14202/) (2023)
- [Can Current Task-oriented Dialogue Models Automate Real-world Scenarios in the Wild?](https://mlsanity.com/paper/2212.10504/) (2022)
- [WIRE: Profiling Witnessed Within-Policy Instruction Collisions in LLM Agents](https://mlsanity.com/paper/2605.27784/) (2026)

## Abstract (arXiv)

To understand diverse natural language commands, virtual assistants today are trained with numerous labor-intensive, manually annotated sentences. This paper presents a methodology and the Genie toolkit that can handle new compound commands with significantly less manual effort. We advocate formalizing the capability of virtual assistants with a Virtual Assistant Programming Language (VAPL) and using a neural semantic parser to translate natural language into VAPL code. Genie needs only a small realistic set of input sentences for validating the neural model. Developers write templates to synthesize data; Genie uses crowdsourced paraphrases and data augmentation, along with the synthesized data, to train a semantic parser. We also propose design principles that make VAPL languages amenable to natural language translation. We apply these principles to revise ThingTalk, the language used by the Almond virtual assistant. We use Genie to build the first semantic parser that can support compound virtual assistants commands with unquoted free-form parameters. Genie achieves a 62% accuracy on realistic user inputs. We demonstrate Genie's generality by showing a 19% and 31% improvement over the previous state of the art on a music skill, aggregate functions, and access control.