# Learning Programmatic Idioms for Scalable Semantic Parsing

arXiv: https://arxiv.org/abs/1904.09086 · HTML: https://mlsanity.com/paper/1904.09086/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2019.

## Built on by (most cited first, 17 of 17)

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- [Deep Learning Meets Software Engineering: A Survey on Pre-Trained Models of Source Code](https://mlsanity.com/paper/2205.11739/) (2022)
- [An Empirical Study of Retrieval-Augmented Code Generation: Challenges and Opportunities](https://mlsanity.com/paper/2501.13742/) (2025)
- [Total Recall: a Customized Continual Learning Method for Neural Semantic Parsers](https://mlsanity.com/paper/2109.05186/) (2021)
- [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)
- [Few-Shot Semantic Parsing for New Predicates](https://mlsanity.com/paper/2101.10708/) (2021)
- [Code Generation for Unknown Libraries via Reading API Documentations](https://mlsanity.com/paper/2202.07806/) (2022)
- [Transformer with Tree-order Encoding for Neural Program Generation](https://mlsanity.com/paper/2206.13354/) (2022)
- [Bayesian Program Learning by Decompiling Amortized Knowledge](https://mlsanity.com/paper/2306.07856/) (2023)
- [Evaluating How Fine-tuning on Bimodal Data Effects Code Generation](https://mlsanity.com/paper/2211.07842/) (2022)
- [Procedures as Programs: Hierarchical Control of Situated Agents through Natural Language](https://mlsanity.com/paper/2109.08214/) (2021)
- [Programming by Example and Text-to-Code Translation for Conversational Code Generation](https://mlsanity.com/paper/2211.11554/) (2022)

## Abstract (arXiv)

Programmers typically organize executable source code using high-level coding patterns or idiomatic structures such as nested loops, exception handlers and recursive blocks, rather than as individual code tokens. In contrast, state of the art (SOTA) semantic parsers still map natural language instructions to source code by building the code syntax tree one node at a time. In this paper, we introduce an iterative method to extract code idioms from large source code corpora by repeatedly collapsing most-frequent depth-2 subtrees of their syntax trees, and train semantic parsers to apply these idioms during decoding. Applying idiom-based decoding on a recent context-dependent semantic parsing task improves the SOTA by 2.2% BLEU score while reducing training time by more than 50%. This improved speed enables us to scale up the model by training on an extended training set that is 5× larger, to further move up the SOTA by an additional 2.3% BLEU and 0.9% exact match. Finally, idioms also significantly improve accuracy of semantic parsing to SQL on the ATIS-SQL dataset, when training data is limited.