mlsanityData to 7 Oct 2026

Naturalizing a Programming Language via Interactive Learning

arXiv 1704.06956 ↗First posted 23 Apr 2017cs.CL cs.AI cs.HCcited by 20

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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.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Apr 2017.

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Citing papers per year: 2021: 9; 2022: 4; 2023: 3; 2024: 3; 2025: 1.

20 papers.

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  1. 2022CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilitiescited by 245
  2. 2021ELLA: Exploration through Learned Language Abstractioncited by 42
  3. 2021LILA: Language-Informed Latent Actionscited by 27
  4. 2021Learning to communicate about shared procedural abstractionscited by 15
  5. 2021Reference-Centric Models for Grounded Collaborative Dialoguecited by 15
  6. 2021Draw Me a Flower: Processing and Grounding Abstraction in Natural Languagecited by 14
  7. 2021On The Ingredients of an Effective Zero-shot Semantic Parsercited by 13
  8. 2023ANPL: Towards Natural Programming with Interactive Decompositioncited by 13
  9. 2024Synthesizing Programmatic Reinforcement Learning Policies with Large Language Model Guided Searchcited by 13
  10. 2022Pragmatics in Language Grounding: Phenomena, Tasks, and Modeling Approachescited by 12
  11. 2021Analysis of Language Change in Collaborative Instruction Followingcited by 9
  12. 2024Vocal Sandbox: Continual Learning and Adaptation for Situated Human-Robot Collaborationcited by 4
8 more
  1. 2021droidlet: modular, heterogenous, multi-modal agentscited by 2
  2. 2025ProVox: Personalization and Proactive Planning for Situated Human-Robot Collaborationcited by 2
  3. 2022Many Episode Learning in a Modular Embodied Agent via End-to-End Interactioncited by 1
  4. 2022Counterfactual Explanations for Natural Language Interfacescited by 1
  5. 2023Learning a Hierarchical Planner from Humans in Multiple Generationscited by 1
  6. 2024IDAT: A Multi-Modal Dataset and Toolkit for Building and Evaluating Interactive Task-Solving Agentscited by 1
  7. 2021Neural Abstructions: Abstractions that Support Construction for Grounded Language Learningcited by 0
  8. 2023Exploring Automatically Perturbed Natural Language Explanations in Relation Extractioncited by 0