mlsanityData to 7 Oct 2026

Good-Enough Compositional Data Augmentation

arXiv 1904.09545 ↗First posted 21 Apr 2019cs.CLcited by 158

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Abstract (arXiv)

We propose a simple data augmentation protocol aimed at providing a compositional inductive bias in conditional and unconditional sequence models. Under this protocol, synthetic training examples are constructed by taking real training examples and replacing (possibly discontinuous) fragments with other fragments that appear in at least one similar environment. The protocol is model-agnostic and useful for a variety of tasks. Applied to neural sequence-to-sequence models, it reduces error rate by as much as 87% on diagnostic tasks from the SCAN dataset and 16% on a semantic parsing task. Applied to n-gram language models, it reduces perplexity by roughly 1% on small corpora in several languages.

Key facts. Cited by 158 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Apr 2019.

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Citing papers per year: 2021: 52; 2022: 49; 2023: 38; 2024: 10; 2025: 7; 2026: 2.

158 papers.

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  1. 2022Least-to-Most Prompting Enables Complex Reasoning in Large Language Modelscited by 1,397
  2. 2021A Survey of Data Augmentation Approaches for NLPcited by 408
  3. 2022Unnatural Instructions: Tuning Language Models with (Almost) No Human Laborcited by 395
  4. 2023From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thoughtcited by 118
  5. 2021The Devil is in the Detail: Simple Tricks Improve Systematic Generalization of Transformerscited by 117
  6. 2021A Survey on Data Augmentation for Text Classificationcited by 111
  7. 2021An Empirical Survey of Data Augmentation for Limited Data Learning in NLPcited by 101
  8. 2022Does CLIP Bind Concepts? Probing Compositionality in Large Image Modelscited by 101
  9. 2023DyVal: Dynamic Evaluation of Large Language Models for Reasoning Taskscited by 100
  10. 2022Compositional Semantic Parsing with Large Language Modelscited by 95
  11. 2021Data Augmentation Approaches in Natural Language Processing: A Surveycited by 82
  12. 2021Understanding by Understanding Not: Modeling Negation in Language Modelscited by 73
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  1. 2021Making Transformers Solve Compositional Taskscited by 72
  2. 2021Tailor: Generating and Perturbing Text with Semantic Controlscited by 67
  3. 2021AEDA: An Easier Data Augmentation Technique for Text Classificationcited by 67
  4. 2021Meta-Learning to Compositionally Generalizecited by 62
  5. 2022Compositional Temporal Grounding with Structured Variational Cross-Graph Correspondence Learningcited by 59
  6. 2021The paradox of the compositionality of natural language: a neural machine translation case studycited by 58
  7. 2021Unlocking Compositional Generalization in Pre-trained Models Using Intermediate Representationscited by 55
  8. 2021Neural Data Augmentation via Example Extrapolationcited by 54
  9. 2023How Do In-Context Examples Affect Compositional Generalization?cited by 51
  10. 2022Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsingcited by 50
  11. 2021Improving Compositional Generalization with Latent Structure and Data Augmentationcited by 46
  12. 2021ELLA: Exploration through Learned Language Abstractioncited by 42
  13. 2021An Investigation of the (In)effectiveness of Counterfactually Augmented Datacited by 42
  14. 2021Learning to Synthesize Data for Semantic Parsingcited by 41
  15. 2021Turning Tables: Generating Examples from Semi-structured Tables for Endowing Language Models with Reasoning Skillscited by 39
  16. 2021Systematic Generalization with Edge Transformerscited by 38
  17. 2022SeqZero: Few-shot Compositional Semantic Parsing with Sequential Prompts and Zero-shot Modelscited by 38
  18. 2024A Comprehensive Survey on Data Augmentationcited by 38
  19. 2021Few-Shot Text Classification with Triplet Networks, Data Augmentation, and Curriculum Learningcited by 35
  20. 2021Controllable Semantic Parsing via Retrieval Augmentationcited by 35
  21. 2021On Compositional Generalization of Neural Machine Translationcited by 34
  22. 2023Skills-in-Context Prompting: Unlocking Compositionality in Large Language Modelscited by 33
  23. 2021HiddenCut: Simple Data Augmentation for Natural Language Understanding with Better Generalizationcited by 31
  24. 2021Learning Algebraic Recombination for Compositional Generalizationcited by 31
  25. 2021Sequence-to-Sequence Learning with Latent Neural Grammarscited by 30
  26. 2021Disentangled Sequence to Sequence Learning for Compositional Generalizationcited by 30
  27. 2022Unobserved Local Structures Make Compositional Generalization Hardcited by 29
  28. 2021Lexicon Learning for Few-Shot Neural Sequence Modelingcited by 28
  29. 2021Evaluating Gender Bias in Natural Language Inferencecited by 27
  30. 2021Finding needles in a haystack: Sampling Structurally-diverse Training Sets from Synthetic Data for Compositional Generalizationcited by 27
  31. 2021Symbolic Brittleness in Sequence Models: on Systematic Generalization in Symbolic Mathematicscited by 27
  32. 2023InstructProtein: Aligning Human and Protein Language via Knowledge Instructioncited by 27
  33. 2021Substructure Substitution: Structured Data Augmentation for NLPcited by 25
  34. 2021Incorporating External Knowledge to Enhance Tabular Reasoningcited by 23
  35. 2022NeuroCounterfactuals: Beyond Minimal-Edit Counterfactuals for Richer Data Augmentationcited by 23
  36. 2023Improving Generalization in Language Model-Based Text-to-SQL Semantic Parsing: Two Simple Semantic Boundary-Based Techniquescited by 23
  37. 2022Equi-Tuning: Group Equivariant Fine-Tuning of Pretrained Modelscited by 22
  38. 2024A Philosophical Introduction to Language Models -- Part I: Continuity With Classic Debatescited by 22

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