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Substructure Substitution: Structured Data Augmentation for NLP

arXiv 2101.00411 ↗First posted 2 Jan 2021cs.CLcited by 25

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Abstract (arXiv)

We study a family of data augmentation methods, substructure substitution (SUB2), for natural language processing (NLP) tasks. SUB2 generates new examples by substituting substructures (e.g., subtrees or subsequences) with ones with the same label, which can be applied to many structured NLP tasks such as part-of-speech tagging and parsing. For more general tasks (e.g., text classification) which do not have explicitly annotated substructures, we present variations of SUB2 based on constituency parse trees, introducing structure-aware data augmentation methods to general NLP tasks. For most cases, training with the augmented dataset by SUB2 achieves better performance than training with the original training set. Further experiments show that SUB2 has more consistent performance than other investigated augmentation methods, across different tasks and sizes of the seed dataset.

Key facts. Cited by 25 papers in the map (data through 14 Oct 2026). Builds on 48 of its 80 references that are in the map. First posted on arXiv 2 Jan 2021.

Builds on

48 of 80 references.

References · 48

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,183
  4. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
  5. 2015Character-level Convolutional Networks for Text Classificationcited by 2,330
  6. 2014Convolutional Neural Networks for Sentence Classificationcited by 1,127
  7. 2019Unsupervised Data Augmentation for Consistency Trainingcited by 1,060
  8. 2019EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Taskscited by 807
  9. 2015Improving Neural Machine Translation Models with Monolingual Datacited by 788
  10. 2018Generating Natural Language Adversarial Examplescited by 424
  11. 2016Deep Biaffine Attention for Neural Dependency Parsingcited by 334
  12. 2015Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networkscited by 306
36 more
  1. 2017Synthetic and Natural Noise Both Break Neural Machine Translationcited by 306
  2. 2018Adversarial Example Generation with Syntactically Controlled Paraphrase Networkscited by 286
  3. 2020Universal Dependencies v2: An Evergrowing Multilingual Treebank Collectioncited by 271
  4. 2018Gender Bias in Neural Natural Language Processingcited by 211
  5. 2018Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relationscited by 201
  6. 2019Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphologycited by 200
  7. 2018Constituency Parsing with a Self-Attentive Encodercited by 199
  8. 2019Good-Enough Compositional Data Augmentationcited by 158
  9. 2017Data Augmentation for Low-Resource Neural Machine Translationcited by 139
  10. 2019Augmenting Data with Mixup for Sentence Classification: An Empirical Studycited by 125
  11. 2018Style Transfer Through Back-Translationcited by 124
  12. 2016Data Recombination for Neural Semantic Parsingcited by 111
  13. 2020Syntactic Data Augmentation Increases Robustness to Inference Heuristicscited by 86
  14. 2020Logic-Guided Data Augmentation and Regularization for Consistent Question Answeringcited by 78
  15. 2020An Analysis of Simple Data Augmentation for Named Entity Recognitioncited by 76
  16. 2018SwitchOut: an Efficient Data Augmentation Algorithm for Neural Machine Translationcited by 72
  17. 2020Sequence-Level Mixed Sample Data Augmentationcited by 71
  18. 2018Sequence-to-Sequence Data Augmentation for Dialogue Language Understandingcited by 68
  19. 2019Generalized Data Augmentation for Low-Resource Translationcited by 53
  20. 2019Soft Contextual Data Augmentation for Neural Machine Translationcited by 49
  21. 2020Generating Fluent Adversarial Examples for Natural Languagescited by 49
  22. 2020Efficient Second-Order TreeCRF for Neural Dependency Parsingcited by 48
  23. 2020Data Boost: Text Data Augmentation Through Reinforcement Learning Guided Conditional Generationcited by 47
  24. 2020Parallel Data Augmentation for Formality Style Transfercited by 39
  25. 2020Local Additivity Based Data Augmentation for Semi-supervised NERcited by 31
  26. 2017Semi-Supervised QA with Generative Domain-Adaptive Netscited by 27
  27. 2019Cooperative Learning of Disjoint Syntax and Semanticscited by 27
  28. 2016Improved Relation Classification by Deep Recurrent Neural Networks with Data Augmentationcited by 26
  29. 2019Automatically Learning Data Augmentation Policies for Dialogue Taskscited by 20
  30. 2020Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous Spacecited by 19
  31. 2020On the Role of Supervision in Unsupervised Constituency Parsingcited by 18
  32. 2018On Tree-Based Neural Sentence Modelingcited by 16
  33. 2019Sequence Tagging with Contextual and Non-Contextual Subword Representations: A Multilingual Evaluationcited by 14
  34. 2018Learning Visually-Grounded Semantics from Contrastive Adversarial Samplescited by 13
  35. 2020Variational Hierarchical Dialog Autoencoder for Dialog State Tracking Data Augmentationcited by 9
  36. 2018Improved Sentence Modeling using Suffix Bidirectional LSTMcited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 5; 2022: 7; 2023: 6; 2024: 5; 2025: 1; 2026: 1.

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  1. 2021A Survey of Data Augmentation Approaches for NLPcited by 409
  2. 2021A Survey on Data Augmentation for Text Classificationcited by 111
  3. 2021Data Augmentation Approaches in Natural Language Processing: A Surveycited by 82
  4. 2021NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentationcited by 73
  5. 2022Contrastive Learning Reduces Hallucination in Conversationscited by 40
  6. 2024A Comprehensive Survey on Data Augmentationcited by 40
  7. 2025Pre-training under infinite computecited by 29
  8. 2023Exploring New Frontiers in Agricultural NLP: Investigating the Potential of Large Language Models for Food Applicationscited by 21
  9. 2022SUBS: Subtree Substitution for Compositional Semantic Parsingcited by 16
  10. 2022TreeMix: Compositional Constituency-based Data Augmentation for Natural Language Understandingcited by 15
  11. 2022EPiDA: An Easy Plug-in Data Augmentation Framework for High Performance Text Classificationcited by 7
  12. 2022Hierarchical Phrase-based Sequence-to-Sequence Learningcited by 7
13 more
  1. 2026Limited Linguistic Diversity in Embodied AI Datasetscited by 6
  2. 2021Substructure Distribution Projection for Zero-Shot Cross-Lingual Dependency Parsingcited by 5
  3. 2022Distribution augmentation for low-resource expressive text-to-speechcited by 5
  4. 2024WSC+: Enhancing The Winograd Schema Challenge Using Tree-of-Expertscited by 5
  5. 2023A Unified Framework for Generative Data Augmentation: A Comprehensive Surveycited by 4
  6. 2024How Well Do LLMs Handle Cantonese? Benchmarking Cantonese Capabilities of Large Language Modelscited by 3
  7. 2023Data Augmentation for Neural NLPcited by 2
  8. 2023TreeSwap: Data Augmentation for Machine Translation via Dependency Subtree Swappingcited by 2
  9. 2024EvoGrad: A Dynamic Take on the Winograd Schema Challenge with Human Adversariescited by 2
  10. 2024Learning Language Structures through Groundingcited by 2
  11. 2023Data Augmentation for Machine Translation via Dependency Subtree Swappingcited by 1
  12. 2022Feature Weaken: Vicinal Data Augmentation for Classificationcited by 0
  13. 2023SegMix: A Simple Structure-Aware Data Augmentation Methodcited by 0