# Program Synthesis and Semantic Parsing with Learned Code Idioms

arXiv: https://arxiv.org/abs/1906.10816 · HTML: https://mlsanity.com/paper/1906.10816/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2019.

## Built on by (most cited first, 30 of 34)

- [Inducing Programmatic Skills for Agentic Tasks](https://mlsanity.com/paper/2504.06821/) (2025)
- [Symbolic Regression with a Learned Concept Library](https://mlsanity.com/paper/2409.09359/) (2024)
- [TroVE: Inducing Verifiable and Efficient Toolboxes for Solving Programmatic Tasks](https://mlsanity.com/paper/2401.12869/) (2024)
- [Top-Down Synthesis for Library Learning](https://mlsanity.com/paper/2211.16605/) (2022)
- [LILO: Learning Interpretable Libraries by Compressing and Documenting Code](https://mlsanity.com/paper/2310.19791/) (2023)
- [Leveraging Language to Learn Program Abstractions and Search Heuristics](https://mlsanity.com/paper/2106.11053/) (2021)
- [Securing Large Language Models: Threats, Vulnerabilities and Responsible Practices](https://mlsanity.com/paper/2403.12503/) (2024)
- [Towards Data-and Knowledge-Driven Artificial Intelligence: A Survey on Neuro-Symbolic Computing](https://mlsanity.com/paper/2210.15889/) (2022)
- [A Hazard Analysis Framework for Code Synthesis Large Language Models](https://mlsanity.com/paper/2207.14157/) (2022)
- [Investigating Math Word Problems using Pretrained Multilingual Language Models](https://mlsanity.com/paper/2105.08928/) (2021)
- [Improving Tree-Structured Decoder Training for Code Generation via Mutual Learning](https://mlsanity.com/paper/2105.14796/) (2021)
- [Exploring Dynamic Selection of Branch Expansion Orders for Code Generation](https://mlsanity.com/paper/2106.00261/) (2021)
- [R-U-SURE? Uncertainty-Aware Code Suggestions By Maximizing Utility Across Random User Intents](https://mlsanity.com/paper/2303.00732/) (2023)
- [Total Recall: a Customized Continual Learning Method for Neural Semantic Parsers](https://mlsanity.com/paper/2109.05186/) (2021)
- [On the Effectiveness of Pretrained Models for API Learning](https://mlsanity.com/paper/2204.03498/) (2022)
- [CodeBPE: Investigating Subtokenization Options for Large Language Model Pretraining on Source Code](https://mlsanity.com/paper/2308.00683/) (2023)
- [Toward Code Generation: A Survey and Lessons from Semantic Parsing](https://mlsanity.com/paper/2105.03317/) (2021)
- [A Simple, Yet Effective Approach to Finding Biases in Code Generation](https://mlsanity.com/paper/2211.00609/) (2022)
- [Transcribing Natural Languages for The Deaf via Neural Editing Programs](https://mlsanity.com/paper/2112.09600/) (2021)
- [On the Limitations of Embedding Based Methods for Measuring Functional Correctness for Code Generation](https://mlsanity.com/paper/2405.01580/) (2024)
- [Predictive Querying for Autoregressive Neural Sequence Models](https://mlsanity.com/paper/2210.06464/) (2022)
- [Autoencoders as Tools for Program Synthesis](https://mlsanity.com/paper/2108.07129/) (2021)
- [Full Line Code Completion: Bringing AI to Desktop](https://mlsanity.com/paper/2405.08704/) (2024)
- [Generating Planning Feedback for Open-Ended Programming Exercises with LLMs](https://mlsanity.com/paper/2504.08958/) (2025)
- [Neural Machine Translation for Code Generation](https://mlsanity.com/paper/2305.13504/) (2023)
- [kNN-ICL: Compositional Task-Oriented Parsing Generalization with Nearest Neighbor In-Context Learning](https://mlsanity.com/paper/2312.10771/) (2023)
- [Mapping Language to Programs using Multiple Reward Components with Inverse Reinforcement Learning](https://mlsanity.com/paper/2110.00842/) (2021)
- [Bayesian Program Learning by Decompiling Amortized Knowledge](https://mlsanity.com/paper/2306.07856/) (2023)
- [Procedures as Programs: Hierarchical Control of Situated Agents through Natural Language](https://mlsanity.com/paper/2109.08214/) (2021)
- [Iterative Genetic Improvement: Scaling Stochastic Program Synthesis](https://mlsanity.com/paper/2202.13040/) (2022)

## Abstract (arXiv)

Program synthesis of general-purpose source code from natural language specifications is challenging due to the need to reason about high-level patterns in the target program and low-level implementation details at the same time. In this work, we present PATOIS, a system that allows a neural program synthesizer to explicitly interleave high-level and low-level reasoning at every generation step. It accomplishes this by automatically mining common code idioms from a given corpus, incorporating them into the underlying language for neural synthesis, and training a tree-based neural synthesizer to use these idioms during code generation. We evaluate PATOIS on two complex semantic parsing datasets and show that using learned code idioms improves the synthesizer's accuracy.