mlsanityData to 14 Oct 2026

Data Recombination for Neural Semantic Parsing

arXiv 1606.03622 ↗First posted 11 Jun 2016cs.CLcited by 111

Not on the map. Why?

Abstract (arXiv)

Modeling crisp logical regularities is crucial in semantic parsing, making it difficult for neural models with no task-specific prior knowledge to achieve good results. In this paper, we introduce data recombination, a novel framework for injecting such prior knowledge into a model. From the training data, we induce a high-precision synchronous context-free grammar, which captures important conditional independence properties commonly found in semantic parsing. We then train a sequence-to-sequence recurrent network (RNN) model with a novel attention-based copying mechanism on datapoints sampled from this grammar, thereby teaching the model about these structural properties. Data recombination improves the accuracy of our RNN model on three semantic parsing datasets, leading to new state-of-the-art performance on the standard GeoQuery dataset for models with comparable supervision.

Key facts. Cited by 111 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Jun 2016.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2018: 1; 2019: 0; 2020: 0; 2021: 47; 2022: 29; 2023: 26; 2024: 2; 2025: 5; 2026: 1.

111 papers.

Sort:

By area:

  1. 2021A Survey of Data Augmentation Approaches for NLPcited by 409
  2. 2023A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPTcited by 224
  3. 2021Constrained Language Models Yield Few-Shot Semantic Parserscited by 163
  4. 2023From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thoughtcited by 119
  5. 2021An Empirical Survey of Data Augmentation for Limited Data Learning in NLPcited by 101
  6. 2021Break-It-Fix-It: Unsupervised Learning for Program Repaircited by 71
  7. 2021Meta-Learning to Compositionally Generalizecited by 62
  8. 2023Coupling Large Language Models with Logic Programming for Robust and General Reasoning from Textcited by 59
  9. 2021Unlocking Compositional Generalization in Pre-trained Models Using Intermediate Representationscited by 55
  10. 2021Neural Data Augmentation via Example Extrapolationcited by 54
  11. 2022Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsingcited by 50
  12. 2021Improving Compositional Generalization with Latent Structure and Data Augmentationcited by 46
38 more
  1. 2021Leveraging Language to Learn Program Abstractions and Search Heuristicscited by 45
  2. 2021ELLA: Exploration through Learned Language Abstractioncited by 42
  3. 2021An Investigation of the (In)effectiveness of Counterfactually Augmented Datacited by 42
  4. 2021Learning to Synthesize Data for Semantic Parsingcited by 41
  5. 2021Neural-Symbolic Solver for Math Word Problems with Auxiliary Taskscited by 38
  6. 2022SeqZero: Few-shot Compositional Semantic Parsing with Sequential Prompts and Zero-shot Modelscited by 38
  7. 2021Communicating Natural Programs to Humans and Machinescited by 35
  8. 2021Controllable Semantic Parsing via Retrieval Augmentationcited by 35
  9. 2021Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsingcited by 33
  10. 2023Skills-in-Context Prompting: Unlocking Compositionality in Large Language Modelscited by 33
  11. 2018Improving a Neural Semantic Parser by Counterfactual Learning from Human Bandit Feedbackcited by 32
  12. 2021HiddenCut: Simple Data Augmentation for Natural Language Understanding with Better Generalizationcited by 32
  13. 2021Sequence-to-Sequence Learning with Latent Neural Grammarscited by 30
  14. 2021Disentangled Sequence to Sequence Learning for Compositional Generalizationcited by 30
  15. 2021Lexicon Learning for Few-Shot Neural Sequence Modelingcited by 28
  16. 2022Importance of Synthesizing High-quality Data for Text-to-SQL Parsingcited by 28
  17. 2021SeaD: End-to-end Text-to-SQL Generation with Schema-aware Denoisingcited by 27
  18. 2021Finding needles in a haystack: Sampling Structurally-diverse Training Sets from Synthetic Data for Compositional Generalizationcited by 27
  19. 2021Substructure Substitution: Structured Data Augmentation for NLPcited by 25
  20. 2025Theory of Mind in Large Language Models: Assessment and Enhancementcited by 25
  21. 2022Knowledge Base Question Answering: A Semantic Parsing Perspectivecited by 22
  22. 2022CLASP: Few-Shot Cross-Lingual Data Augmentation for Semantic Parsingcited by 21
  23. 2021GOLD: Improving Out-of-Scope Detection in Dialogues using Data Augmentationcited by 20
  24. 2021Zero-Shot Cross-lingual Semantic Parsingcited by 19
  25. 2022SUBS: Subtree Substitution for Compositional Semantic Parsingcited by 16
  26. 2022TreeMix: Compositional Constituency-based Data Augmentation for Natural Language Understandingcited by 15
  27. 2021On Robustness of Neural Semantic Parserscited by 14
  28. 2021From Paraphrasing to Semantic Parsing: Unsupervised Semantic Parsing via Synchronous Semantic Decodingcited by 14
  29. 2021On The Ingredients of an Effective Zero-shot Semantic Parsercited by 13
  30. 2022QASem Parsing: Text-to-text Modeling of QA-based Semanticscited by 12
  31. 2023Generating Data for Symbolic Language with Large Language Modelscited by 12
  32. 2021Hierarchical Neural Data Synthesis for Semantic Parsingcited by 11
  33. 2021Context Transformer with Stacked Pointer Networks for Conversational Question Answering over Knowledge Graphscited by 10
  34. 2021Low-Resource Task-Oriented Semantic Parsing via Intrinsic Modelingcited by 10
  35. 2021Structured Context and High-Coverage Grammar for Conversational Question Answering over Knowledge Graphscited by 10
  36. 2022Augmenting Multi-Turn Text-to-SQL Datasets with Self-Playcited by 9
  37. 2022Compositionality as Lexical Symmetrycited by 8
  38. 2023Learning to Substitute Spans towards Improving Compositional Generalizationcited by 8

50 most cited of 111 All 111 (JSON)