Data to 14 Oct 2026

TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation

arXiv 1810.02720 ↗First posted 5 Oct 2018cs.CL cs.PLcited by 88

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Abstract (arXiv)

We present TRANX, a transition-based neural semantic parser that maps natural language (NL) utterances into formal meaning representations (MRs). TRANX uses a transition system based on the abstract syntax description language for the target MR, which gives it two major advantages: (1) it is highly accurate, using information from the syntax of the target MR to constrain the output space and model the information flow, and (2) it is highly generalizable, and can easily be applied to new types of MR by just writing a new abstract syntax description corresponding to the allowable structures in the MR. Experiments on four different semantic parsing and code generation tasks show that our system is generalizable, extensible, and effective, registering strong results compared to existing neural semantic parsers.

Key facts. Cited by 88 papers in the map (data through 14 Oct 2026). First posted on arXiv 5 Oct 2018.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 22; 2022: 24; 2023: 15; 2024: 11; 2025: 8; 2026: 8.

88 papers.

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2021PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Modelscited by 411
2022UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Modelscited by 231
2024Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Surveycited by 159
2023Self-Edit: Fault-Aware Code Editor for Code Generationcited by 111
2022Execution-Based Evaluation for Open-Domain Code Generationcited by 91
2022Out of the BLEU: how should we assess quality of the Code Generation models?cited by 65
2023Enhancing Few-shot Text-to-SQL Capabilities of Large Language Models: A Study on Prompt Design Strategiescited by 45
2023Large Language Model-Aware In-Context Learning for Code Generationcited by 43
2021Learning to Synthesize Data for Semantic Parsingcited by 41
2022MCoNaLa: A Benchmark for Code Generation from Multiple Natural Languagescited by 41
2024Automatic Programming: Large Language Models and Beyondcited by 36
2021Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsingcited by 33

38 more

2022MultiSpider: Towards Benchmarking Multilingual Text-to-SQL Semantic Parsingcited by 22
2021The Power of Prompt Tuning for Low-Resource Semantic Parsingcited by 21
2022UniSAr: A Unified Structure-Aware Autoregressive Language Model for Text-to-SQLcited by 20
2025An Empirical Study of Retrieval-Augmented Code Generation: Challenges and Opportunitiescited by 18
2024Enhancing Code Generation Performance of Smaller Models by Distilling the Reasoning Ability of LLMscited by 17
2025GraphCodeAgent: Dual Graph-Guided LLM Agent for Retrieval-Augmented Repo-Level Code Generationcited by 17
2025cAST: Enhancing Code Retrieval-Augmented Generation with Structural Chunking via Abstract Syntax Treecited by 17
2021Learning Structural Edits via Incremental Tree Transformationscited by 16
2021Shellcode_IA32: A Dataset for Automatic Shellcode Generationcited by 16
2021On Robustness of Neural Semantic Parserscited by 14
2021Cost-effective End-to-end Information Extraction for Semi-structured Document Imagescited by 14
2023KNOD: Domain Knowledge Distilled Tree Decoder for Automated Program Repaircited by 13
2023Sequential Query Encoding For Complex Query Answering on Knowledge Graphscited by 13
2022CodeEditor: Learning to Edit Source Code with Pre-trained Modelscited by 12
2022Incorporating Domain Knowledge through Task Augmentation for Front-End JavaScript Code Generationcited by 11
2021Improving Tree-Structured Decoder Training for Code Generation via Mutual Learningcited by 10
2021Exploring Dynamic Selection of Branch Expansion Orders for Code Generationcited by 10
2021Total Recall: a Customized Continual Learning Method for Neural Semantic Parserscited by 9
2021Toward Code Generation: A Survey and Lessons from Semantic Parsingcited by 8
2021Reading StackOverflow Encourages Cheating: Adding Question Text Improves Extractive Code Generationcited by 8
2025Multilingual Text-to-SQL: Benchmarking the Limits of Language Models with Collaborative Language Agentscited by 8
2021Few-Shot Semantic Parsing for New Predicatescited by 7
2021Learning from Executions for Semantic Parsingcited by 7
2022CodePAD: Sequence-based Code Generation with Pushdown Automatoncited by 7
2023Question Answering for Electronic Health Records: A Scoping Review of datasets and modelscited by 7
2021Code Generation from Natural Language with Less Prior and More Monolingual Datacited by 6
2022The impact of lexical and grammatical processing on generating code from natural languagecited by 6
2023InfeRE: Step-by-Step Regex Generation via Chain of Inferencecited by 5
2021Natural Language-Guided Programmingcited by 4
2022A Survey of Deep Learning Models for Structural Code Understandingcited by 4
2022A Library for Representing Python Programs as Graphs for Machine Learningcited by 4
2024Showing LLM-Generated Code Selectively Based on Confidence of LLMscited by 4
2024Fortran2CPP: Automating Fortran-to-C++ Translation using LLMs via Multi-Turn Dialogue and Dual-Agent Integrationcited by 4
2022A Survey on Artificial Intelligence for Source Code: A Dialogue Systems Perspectivecited by 3
2022Transformer with Tree-order Encoding for Neural Program Generationcited by 3
2023NL2CMD: An Updated Workflow for Natural Language to Bash Commands Translationcited by 3
2024Structured Code Representations Enable Data-Efficient Adaptation of Code Language Modelscited by 3
2025Valid Text-to-SQL Generation with Unification-based DeepStochLogcited by 3

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