mlsanityData to 7 Oct 2026

Data Augmentation for Neural Machine Translation using Generative Language Model

arXiv 2307.16833 ↗First posted 26 Jul 2023cs.CL cs.AIcited by 12

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Abstract (arXiv)

Despite the rapid growth in model architecture, the scarcity of large parallel corpora remains the main bottleneck in Neural Machine Translation. Data augmentation is a technique that enhances the performance of data-hungry models by generating synthetic data instead of collecting new ones. We explore prompt-based data augmentation approaches that leverage large-scale language models such as ChatGPT. To create a synthetic parallel corpus, we compare 3 methods using different prompts. We employ two assessment metrics to measure the diversity of the generated synthetic data. This approach requires no further model training cost, which is mandatory in other augmentation methods like back-translation. The proposed method improves the unaugmented baseline by 0.68 BLEU score.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 9 of its 13 references that are in the map. First posted on arXiv 26 Jul 2023.

Builds on

9 of 13 references.

References · 9

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2018A Call for Clarity in Reporting BLEU Scorescited by 1,790
  3. 2015Improving Neural Machine Translation Models with Monolingual Datacited by 784
  4. 2018Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyondcited by 434
  5. 2020Multilingual Translation with Extensible Multilingual Pretraining and Finetuningcited by 287
  6. 2021GPT3Mix: Leveraging Large-scale Language Models for Text Augmentationcited by 185
  7. 2020Data Augmentation using Pre-trained Transformer Modelscited by 168
  8. 2020On Exposure Bias, Hallucination and Domain Shift in Neural Machine Translationcited by 127
  9. 2019Domain Robustness in Neural Machine Translationcited by 64

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 6; 2026: 2.

12 papers.

  1. 2024On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Surveycited by 240
  2. 2024A Survey on Data Augmentation in Large Model Eracited by 31
  3. 2024Data Augmentation using Large Language Models: Data Perspectives, Learning Paradigms and Challengescited by 30
  4. 2024Robust Neural Information Retrieval: An Adversarial and Out-of-distribution Perspectivecited by 15
  5. 2025Scaling Low-Resource MT via Synthetic Data Generation with LLMscited by 14
  6. 2025Data Augmentation With Back translation for Low Resource languages: A case of English and Lugandacited by 3
  7. 2026KD4MT: A Survey of Knowledge Distillation for Machine Translationcited by 2
  8. 2025Data Augmentation and Hyperparameter Tuning for Low-Resource MFAcited by 1
  9. 2025Self-Foveate: Enhancing Diversity and Difficulty of Synthesized Instructions from Unsupervised Text via Multi-Level Foveationcited by 1
  10. 2026Towards Better RL Training Data Utilization via Second-Order Rolloutcited by 1
  11. 2025Step-Opt: Boosting Optimization Modeling in LLMs through Iterative Data Synthesis and Structured Validationcited by 0
  12. 2025From Scarcity to Efficiency: Investigating the Effects of Data Augmentation on African Machine Translationcited by 0