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Data Augmentation via Dependency Tree Morphing for Low-Resource Languages

arXiv 1903.09460 ↗First posted 22 Mar 2019cs.CLcited by 45

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Abstract (arXiv)

Neural NLP systems achieve high scores in the presence of sizable training dataset. Lack of such datasets leads to poor system performances in the case low-resource languages. We present two simple text augmentation techniques using dependency trees, inspired from image processing. We crop sentences by removing dependency links, and we rotate sentences by moving the tree fragments around the root. We apply these techniques to augment the training sets of low-resource languages in Universal Dependencies project. We implement a character-level sequence tagging model and evaluate the augmented datasets on part-of-speech tagging task. We show that crop and rotate provides improvements over the models trained with non-augmented data for majority of the languages, especially for languages with rich case marking systems.

Key facts. Cited by 45 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Mar 2019.

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Citing papers per year: 2021: 13; 2022: 7; 2023: 13; 2024: 8; 2025: 1; 2026: 3.

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  1. 2021A Survey of Data Augmentation Approaches for NLPcited by 408
  2. 2021A Survey on Data Augmentation for Text Classificationcited by 111
  3. 2021Data Augmentation Approaches in Natural Language Processing: A Surveycited by 82
  4. 2021Local Augmentation for Graph Neural Networkscited by 52
  5. 2023LongForm: Effective Instruction Tuning with Reverse Instructionscited by 47
  6. 2024A Comprehensive Survey on Data Augmentationcited by 38
  7. 2021Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsingcited by 32
  8. 2021Data Augmentation in Natural Language Processing: A Novel Text Generation Approach for Long and Short Text Classifierscited by 24
  9. 2021Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systemscited by 16
  10. 2022SUBS: Subtree Substitution for Compositional Semantic Parsingcited by 16
  11. 2021Data Augmentation for Cross-Domain Named Entity Recognitioncited by 14
  12. 2021To Augment or Not to Augment? A Comparative Study on Text Augmentation Techniques for Low-Resource NLPcited by 12
33 more
  1. 2023Mask-then-Fill: A Flexible and Effective Data Augmentation Framework for Event Extractioncited by 12
  2. 2024Retrieval-Augmented Data Augmentation for Low-Resource Domain Taskscited by 12
  3. 2021Data Augmentation for Low-Resource Named Entity Recognition Using Backtranslationcited by 10
  4. 2021Shaking Syntactic Trees on the Sesame Street: Multilingual Probing with Controllable Perturbationscited by 10
  5. 2023GDA: Generative Data Augmentation Techniques for Relation Extraction Taskscited by 10
  6. 2022EPiDA: An Easy Plug-in Data Augmentation Framework for High Performance Text Classificationcited by 7
  7. 2024A Comprehensive Study on NLP Data Augmentation for Hate Speech Detection: Legacy Methods, BERT, and LLMscited by 7
  8. 2022Entity-to-Text based Data Augmentation for various Named Entity Recognition Taskscited by 6
  9. 2021Predicting Different Types of Subtle Toxicity in Unhealthy Online Conversationscited by 5
  10. 2023Bridging the Gap between Decision and Logits in Decision-based Knowledge Distillation for Pre-trained Language Modelscited by 5
  11. 2023Multilingual Nonce Dependency Treebanks: Understanding how Language Models represent and process syntactic structurecited by 5
  12. 2024IndiText Boost: Text Augmentation for Low Resource India Languagescited by 4
  13. 2022Systematic Investigation of Strategies Tailored for Low-Resource Settings for Low-Resource Dependency Parsingcited by 3
  14. 2023xSIM++: An Improved Proxy to Bitext Mining Performance for Low-Resource Languagescited by 3
  15. 2024DropEdge not Foolproof: Effective Augmentation Method for Signed Graph Neural Networkscited by 3
  16. 2023Mitigating Data Scarcity for Large Language Modelscited by 2
  17. 2023Data Augmentation for Neural NLPcited by 2
  18. 2021Developing neural machine translation models for Hungarian-Englishcited by 1
  19. 2023Predicting Visit Cost of Obstructive Sleep Apnea using Electronic Healthcare Records with Transformercited by 1
  20. 2022Natural Language Sentence Generation from API Specificationscited by 0
  21. 2022SpanDrop: Simple and Effective Counterfactual Learning for Long Sequencescited by 0
  22. 2022Entity Aware Syntax Tree Based Data Augmentation for Natural Language Understandingcited by 0
  23. 2023UATTA-EB: Uncertainty-Aware Test-Time Augmented Ensemble of BERTs for Classifying Common Mental Illnesses on Social Media Postscited by 0
  24. 2023SGA: A Graph Augmentation Method for Signed Graph Neural Networkscited by 0
  25. 2023RankAug: Augmented data ranking for text classificationcited by 0
  26. 2023SegMix: A Simple Structure-Aware Data Augmentation Methodcited by 0
  27. 2024Comparing Data Augmentation Methods for End-to-End Task-Oriented Dialog Systemscited by 0
  28. 2024CSSL: Contrastive Self-Supervised Learning for Dependency Parsing on Relatively Free Word Ordered and Morphologically Rich Low Resource Languagescited by 0
  29. 2024Cyber-Attack Technique Classification Using Two-Stage Trained Large Language Modelscited by 0
  30. 2025ARISE: Iterative Rule Induction and Synthetic Data Generation for Text Classificationcited by 0
  31. 2026SAMA: Semantic Anchor-aligned Augmentation for Unified Low-Resource Multimodal Information Extractioncited by 0
  32. 2026A Modular Part-of-Speech Tagger for Scottish Gaelic using spaCycited by 0
  33. 2026Comparing Chunking and Embedding Strategies for Turkish RAG Systemscited by 0