mlsanityData to 7 Oct 2026

The impact of lexical and grammatical processing on generating code from natural language

arXiv 2202.13972 ↗First posted 28 Feb 2022cs.CLcited by 6

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Abstract (arXiv)

Considering the seq2seq architecture of TranX for natural language to code translation, we identify four key components of importance: grammatical constraints, lexical preprocessing, input representations, and copy mechanisms. To study the impact of these components, we use a state-of-the-art architecture that relies on BERT encoder and a grammar-based decoder for which a formalization is provided. The paper highlights the importance of the lexical substitution component in the current natural language to code systems.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 13 of its 19 references that are in the map. First posted on arXiv 28 Feb 2022.

Builds on

13 of 19 references.

References · 13

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  4. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  5. 2021Program Synthesis with Large Language Modelscited by 3,509
  6. 2015Effective Approaches to Attention-based Neural Machine Translationcited by 1,024
  7. 2015Pointer Networkscited by 882
  8. 2021Measuring Coding Challenge Competence With APPScited by 879
  9. 2016Language to Logical Form with Neural Attentioncited by 158
  10. 2017Abstract Syntax Networks for Code Generation and Semantic Parsingcited by 92
  11. 2018TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generationcited by 88
  12. 2021Reading StackOverflow Encourages Cheating: Adding Question Text Improves Extractive Code Generationcited by 8
1 more
  1. 2021Code Generation from Natural Language with Less Prior and More Monolingual Datacited by 6

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Cited by year

Citing papers per year: 2022: 1; 2023: 2; 2024: 2; 2025: 1.

6 papers.

  1. 2024Retrieval-Augmented Generation for AI-Generated Content: A Surveycited by 352
  2. 2022DocPrompting: Generating Code by Retrieving the Docscited by 151
  3. 2023Automatic Readability Assessment for Closely Related Languagescited by 9
  4. 2023InfeRE: Step-by-Step Regex Generation via Chain of Inferencecited by 5
  5. 2024CodeInsight: A Curated Dataset of Practical Coding Solutions from Stack Overflowcited by 4
  6. 2025RETROcode: Leveraging a Code Database for Improved Natural Language to Code Generationcited by 0