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Data Augmentation for Low-Resource Neural Machine Translation

arXiv 1705.00440 ↗First posted 1 May 2017cs.CLcited by 139

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Abstract (arXiv)

The quality of a Neural Machine Translation system depends substantially on the availability of sizable parallel corpora. For low-resource language pairs this is not the case, resulting in poor translation quality. Inspired by work in computer vision, we propose a novel data augmentation approach that targets low-frequency words by generating new sentence pairs containing rare words in new, synthetically created contexts. Experimental results on simulated low-resource settings show that our method improves translation quality by up to 2.9 BLEU points over the baseline and up to 3.2 BLEU over back-translation.

Key facts. Cited by 139 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 May 2017.

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Citing papers per year: 2021: 55; 2022: 26; 2023: 21; 2024: 19; 2025: 13; 2026: 5.

139 papers.

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  1. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,234
  2. 2021A Survey of Data Augmentation Approaches for NLPcited by 409
  3. 2021TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentationcited by 227
  4. 2023PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Promptscited by 226
  5. 2022Learning Video Representations from Large Language Modelscited by 205
  6. 2021GPT3Mix: Leveraging Large-scale Language Models for Text Augmentationcited by 187
  7. 2023Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventionscited by 125
  8. 2021A Survey on Data Augmentation for Text Classificationcited by 111
  9. 2021Neural Machine Translation for Low-Resource Languages: A Surveycited by 109
  10. 2021An Empirical Survey of Data Augmentation for Limited Data Learning in NLPcited by 101
  11. 2021Survey of Low-Resource Machine Translationcited by 92
  12. 2021Data Augmentation Approaches in Natural Language Processing: A Surveycited by 82
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  1. 2021NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentationcited by 73
  2. 2021AEDA: An Easier Data Augmentation Technique for Text Classificationcited by 67
  3. 2022InPars: Data Augmentation for Information Retrieval using Large Language Modelscited by 62
  4. 2021Neural Machine Translation with Monolingual Translation Memorycited by 61
  5. 2021Local Augmentation for Graph Neural Networkscited by 53
  6. 2021Variational Information Bottleneck for Effective Low-Resource Fine-Tuningcited by 51
  7. 2023Synthetic Datasets for Autonomous Driving: A Surveycited by 48
  8. 2021The Low-Resource Double Bind: An Empirical Study of Pruning for Low-Resource Machine Translationcited by 47
  9. 2021Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Surveycited by 46
  10. 2021MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NERcited by 42
  11. 2021Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsingcited by 33
  12. 2021FlipDA: Effective and Robust Data Augmentation for Few-Shot Learningcited by 32
  13. 2024A Survey on Data Augmentation in Large Model Eracited by 31
  14. 2021Continual Mixed-Language Pre-Training for Extremely Low-Resource Neural Machine Translationcited by 29
  15. 2021Robust Attentive Deep Neural Network for Exposing GAN-generated Facescited by 28
  16. 2021Substructure Substitution: Structured Data Augmentation for NLPcited by 25
  17. 2021Data Augmentation in Natural Language Processing: A Novel Text Generation Approach for Long and Short Text Classifierscited by 24
  18. 2021GOLD: Improving Out-of-Scope Detection in Dialogues using Data Augmentationcited by 20
  19. 2024Masked Thought: Simply Masking Partial Reasoning Steps Can Improve Mathematical Reasoning Learning of Language Modelscited by 20
  20. 2022Learning Multimodal Data Augmentation in Feature Spacecited by 18
  21. 2024Empowering Large Language Models for Textual Data Augmentationcited by 17
  22. 2024ToxiCraft: A Novel Framework for Synthetic Generation of Harmful Informationcited by 17
  23. 2021A Survey on Low-Resource Neural Machine Translationcited by 16
  24. 2023Cross-modality Data Augmentation for End-to-End Sign Language Translationcited by 16
  25. 2022TreeMix: Compositional Constituency-based Data Augmentation for Natural Language Understandingcited by 15
  26. 2021Data Augmentation for Cross-Domain Named Entity Recognitioncited by 14
  27. 2021Guiding Generative Language Models for Data Augmentation in Few-Shot Text Classificationcited by 14
  28. 2022Augraphy: A Data Augmentation Library for Document Imagescited by 14
  29. 2023Universal Multimodal Representation for Language Understandingcited by 14
  30. 2025Scaling Low-Resource MT via Synthetic Data Generation with LLMscited by 14
  31. 2021Phrase-level Active Learning for Neural Machine Translationcited by 13
  32. 2022Learning to Generalize to More: Continuous Semantic Augmentation for Neural Machine Translationcited by 13
  33. 2024Low-Resource Vision Challenges for Foundation Modelscited by 13
  34. 2021To Augment or Not to Augment? A Comparative Study on Text Augmentation Techniques for Low-Resource NLPcited by 12
  35. 2022TURJUMAN: A Public Toolkit for Neural Arabic Machine Translationcited by 12
  36. 2021Data Augmentation by Concatenation for Low-Resource Translation: A Mystery and a Solutioncited by 11
  37. 2021Data Augmentation for Low-Resource Named Entity Recognition Using Backtranslationcited by 10
  38. 2021AugLiChem: Data Augmentation Library of Chemical Structures for Machine Learningcited by 10

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