# Naturalizing a Programming Language via Interactive Learning

arXiv: https://arxiv.org/abs/1704.06956 · HTML: https://mlsanity.com/paper/1704.06956/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2017.

## Built on by (most cited first, 20 of 20)

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- [On The Ingredients of an Effective Zero-shot Semantic Parser](https://mlsanity.com/paper/2110.08381/) (2021)
- [ANPL: Towards Natural Programming with Interactive Decomposition](https://mlsanity.com/paper/2305.18498/) (2023)
- [Synthesizing Programmatic Reinforcement Learning Policies with Large Language Model Guided Search](https://mlsanity.com/paper/2405.16450/) (2024)
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- [droidlet: modular, heterogenous, multi-modal agents](https://mlsanity.com/paper/2101.10384/) (2021)
- [ProVox: Personalization and Proactive Planning for Situated Human-Robot Collaboration](https://mlsanity.com/paper/2506.12248/) (2025)
- [Many Episode Learning in a Modular Embodied Agent via End-to-End Interaction](https://mlsanity.com/paper/2204.08687/) (2022)
- [Counterfactual Explanations for Natural Language Interfaces](https://mlsanity.com/paper/2204.13192/) (2022)
- [Learning a Hierarchical Planner from Humans in Multiple Generations](https://mlsanity.com/paper/2310.11614/) (2023)
- [IDAT: A Multi-Modal Dataset and Toolkit for Building and Evaluating Interactive Task-Solving Agents](https://mlsanity.com/paper/2407.08898/) (2024)
- [Neural Abstructions: Abstractions that Support Construction for Grounded Language Learning](https://mlsanity.com/paper/2107.09285/) (2021)
- [Exploring Automatically Perturbed Natural Language Explanations in Relation Extraction](https://mlsanity.com/paper/2305.15520/) (2023)

## Abstract (arXiv)

Our goal is to create a convenient natural language interface for performing well-specified but complex actions such as analyzing data, manipulating text, and querying databases. However, existing natural language interfaces for such tasks are quite primitive compared to the power one wields with a programming language. To bridge this gap, we start with a core programming language and allow users to "naturalize" the core language incrementally by defining alternative, more natural syntax and increasingly complex concepts in terms of compositions of simpler ones. In a voxel world, we show that a community of users can simultaneously teach a common system a diverse language and use it to build hundreds of complex voxel structures. Over the course of three days, these users went from using only the core language to using the naturalized language in 85.9% of the last 10K utterances.