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

## TL;DR

Genie is a toolkit that efficiently generates semantic parsers for virtual assistant commands, reducing manual effort and enabling support for complex, compound commands with high accuracy.

## Contribution

The paper introduces Genie, a novel methodology and toolkit for automatic semantic parser generation using a formal language and data augmentation, improving support for complex commands.

## Key findings

- Genie achieves 62% accuracy on realistic user inputs.
- It outperforms previous methods by 19% and 31% on specific tasks.
- Genie supports compound commands with unquoted free-form parameters.

## Abstract

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.