mlsanityData to 14 Oct 2026

Dynamic Data Selection for Neural Machine Translation

arXiv 1708.00712 ↗First posted 2 Aug 2017cs.CLcited by 33

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Abstract (arXiv)

Intelligent selection of training data has proven a successful technique to simultaneously increase training efficiency and translation performance for phrase-based machine translation (PBMT). With the recent increase in popularity of neural machine translation (NMT), we explore in this paper to what extent and how NMT can also benefit from data selection. While state-of-the-art data selection (Axelrod et al., 2011) consistently performs well for PBMT, we show that gains are substantially lower for NMT. Next, we introduce dynamic data selection for NMT, a method in which we vary the selected subset of training data between different training epochs. Our experiments show that the best results are achieved when applying a technique we call gradual fine-tuning, with improvements up to +2.6 BLEU over the original data selection approach and up to +3.1 BLEU over a general baseline.

Key facts. Cited by 33 papers in the map (data through 14 Oct 2026). First posted on arXiv 2 Aug 2017.

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Citing papers per year: 2021: 9; 2022: 9; 2023: 7; 2024: 5; 2025: 1; 2026: 2.

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  1. 2021Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Surveycited by 46
  2. 2021Prevent the Language Model from being Overconfident in Neural Machine Translationcited by 34
  3. 2022AdaPrompt: Adaptive Model Training for Prompt-based NLPcited by 30
  4. 2021Meta-Curriculum Learning for Domain Adaptation in Neural Machine Translationcited by 28
  5. 2023CombLM: Adapting Black-Box Language Models through Small Fine-Tuned Modelscited by 26
  6. 2022On the Domain Adaptation and Generalization of Pretrained Language Models: A Surveycited by 23
  7. 2023Make Every Example Count: On the Stability and Utility of Self-Influence for Learning from Noisy NLP Datasetscited by 19
  8. 2021Bandits Don't Follow Rules: Balancing Multi-Facet Machine Translation with Multi-Armed Banditscited by 16
  9. 2021The Trade-offs of Domain Adaptation for Neural Language Modelscited by 14
  10. 2022Data Selection Curriculum for Neural Machine Translationcited by 11
  11. 2022Automatic Document Selection for Efficient Encoder Pretrainingcited by 11
  12. 2023Adaptive Training Distributions with Scalable Online Bilevel Optimizationcited by 11
21 more
  1. 2021On the Complementarity of Data Selection and Fine Tuning for Domain Adaptationcited by 10
  2. 2022Improving Automated Program Repair with Domain Adaptationcited by 10
  3. 2024Critical Learning Periods: Leveraging Early Training Dynamics for Efficient Data Pruningcited by 10
  4. 2021Gradient-guided Loss Masking for Neural Machine Translationcited by 8
  5. 2024Quality and Quantity of Machine Translation References for Automatic Metricscited by 7
  6. 2021Selecting Parallel In-domain Sentences for Neural Machine Translation Using Monolingual Textscited by 4
  7. 2022Domain Adaptation of Machine Translation with Crowdworkerscited by 3
  8. 2021Secoco: Self-Correcting Encoding for Neural Machine Translationcited by 2
  9. 2022m4Adapter: Multilingual Multi-Domain Adaptation for Machine Translation with a Meta-Adaptercited by 2
  10. 2022Robust Domain Adaptation for Pre-trained Multilingual Neural Machine Translation Modelscited by 2
  11. 2026SAGE: Sustainable Agent-Guided Expert-tuning for Culturally Attuned Translation in Low-Resource Southeast Asiacited by 2
  12. 2023Separating the Wheat from the Chaff with BREAD: An open-source benchmark and metrics to detect redundancy in textcited by 1
  13. 2023Comprehensive Benchmarking of Entropy and Margin Based Scoring Metrics for Data Selectioncited by 1
  14. 2024LexMatcher: Dictionary-centric Data Collection for LLM-based Machine Translationcited by 1
  15. 2024Clustering Algorithms and RAG Enhancing Semi-Supervised Text Classification with Large LLMscited by 1
  16. 2025Dynamic Jointly Batch Selection for Data Efficient Machine Translation Fine-Tuningcited by 1
  17. 2022Domain Curricula for Code-Switched MT at MixMT 2022cited by 0
  18. 2023Label-Free Multi-Domain Machine Translation with Stage-wise Trainingcited by 0
  19. 2023Source Prompt: Coordinated Pre-training of Language Models on Diverse Corpora from Multiple Sourcescited by 0
  20. 2024TextGram: Towards a better domain-adaptive pretrainingcited by 0
  21. 2026SalamandraTA at WMT 2026 Terminology Shared Task: Hard Examples Are Better Teacherscited by 0