mlsanityData to 7 Oct 2026

Denoising Neural Machine Translation Training with Trusted Data and Online Data Selection

arXiv 1809.00068 ↗First posted 31 Aug 2018cs.CL cs.LG stat.MLcited by 43

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Abstract (arXiv)

Measuring domain relevance of data and identifying or selecting well-fit domain data for machine translation (MT) is a well-studied topic, but denoising is not yet. Denoising is concerned with a different type of data quality and tries to reduce the negative impact of data noise on MT training, in particular, neural MT (NMT) training. This paper generalizes methods for measuring and selecting data for domain MT and applies them to denoising NMT training. The proposed approach uses trusted data and a denoising curriculum realized by online data selection. Intrinsic and extrinsic evaluations of the approach show its significant effectiveness for NMT to train on data with severe noise.

Key facts. Cited by 43 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Aug 2018.

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Citing papers per year: 2021: 13; 2022: 8; 2023: 12; 2024: 9; 2025: 0; 2026: 1.

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  1. 2021Quality at a Glance: An Audit of Web-Crawled Multilingual Datasetscited by 241
  2. 2021High Quality Rather than High Model Probability: Minimum Bayes Risk Decoding with Neural Metricscited by 104
  3. 2023The unreasonable effectiveness of few-shot learning for machine translationcited by 91
  4. 2023Epsilon Sampling Rocks: Investigating Sampling Strategies for Minimum Bayes Risk Decoding for Machine Translationcited by 57
  5. 2022Data Scaling Laws in NMT: The Effect of Noise and Architecturecited by 55
  6. 2021Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Surveycited by 46
  7. 2023MBR and QE Finetuning: Training-time Distillation of the Best and Most Expensive Decoding Methodscited by 33
  8. 2022AdaPrompt: Adaptive Model Training for Prompt-based NLPcited by 30
  9. 2023Scaling Laws for Multilingual Neural Machine Translationcited by 27
  10. 2022On the Domain Adaptation and Generalization of Pretrained Language Models: A Surveycited by 22
  11. 2023Aligning Neural Machine Translation Models: Human Feedback in Training and Inferencecited by 19
  12. 2023Make Every Example Count: On the Stability and Utility of Self-Influence for Learning from Noisy NLP Datasetscited by 18
31 more
  1. 2024Task-Adaptive Pretrained Language Models via Clustered-Importance Samplingcited by 18
  2. 2023In-Context Demonstration Selection with Cross Entropy Differencecited by 17
  3. 2021Bandits Don't Follow Rules: Balancing Multi-Facet Machine Translation with Multi-Armed Banditscited by 16
  4. 2021The Trade-offs of Domain Adaptation for Neural Language Modelscited by 14
  5. 2022Understanding and Improving Sequence-to-Sequence Pretraining for Neural Machine Translationcited by 14
  6. 2022Data Selection Curriculum for Neural Machine Translationcited by 11
  7. 2023Adaptive Training Distributions with Scalable Online Bilevel Optimizationcited by 11
  8. 2021On the Complementarity of Data Selection and Fine Tuning for Domain Adaptationcited by 10
  9. 2022Improving Automated Program Repair with Domain Adaptationcited by 10
  10. 2024Critical Learning Periods: Leveraging Early Training Dynamics for Efficient Data Pruningcited by 10
  11. 2021Gradient-guided Loss Masking for Neural Machine Translationcited by 8
  12. 2021Mixed Cross Entropy Loss for Neural Machine Translationcited by 8
  13. 2024Re-Examine Distantly Supervised NER: A New Benchmark and a Simple Approachcited by 5
  14. 2021Token-wise Curriculum Learning for Neural Machine Translationcited by 4
  15. 2022CLID: Controlled-Length Image Descriptions with Limited Datacited by 4
  16. 2024Need a Small Specialized Language Model? Plan Early!cited by 4
  17. 2024How to Learn in a Noisy World? Self-Correcting the Real-World Data Noise in Machine Translationcited by 4
  18. 2022Analyzing the Use of Influence Functions for Instance-Specific Data Filtering in Neural Machine Translationcited by 3
  19. 2024A Bayesian Approach to Data Point Selectioncited by 3
  20. 2021Secoco: Self-Correcting Encoding for Neural Machine Translationcited by 2
  21. 2021Learning Feature Weights using Reward Modeling for Denoising Parallel Corporacited by 1
  22. 2021Verdi: Quality Estimation and Error Detection for Bilingual Corporacited by 1
  23. 2021Using Machine Translation to Localize Task Oriented NLG Outputcited by 1
  24. 2023Exploiting Multilingualism in Low-resource Neural Machine Translation via Adversarial Learningcited by 1
  25. 2023Separating the Wheat from the Chaff with BREAD: An open-source benchmark and metrics to detect redundancy in textcited by 1
  26. 2024LexMatcher: Dictionary-centric Data Collection for LLM-based Machine Translationcited by 1
  27. 2024Curriculum Learning for Cross-Lingual Data-to-Text Generation With Noisy Datacited by 1
  28. 2023Epi-Curriculum: Episodic Curriculum Learning for Low-Resource Domain Adaptation in Neural Machine Translationcited by 0
  29. 2023Source Prompt: Coordinated Pre-training of Language Models on Diverse Corpora from Multiple Sourcescited by 0
  30. 2024Optimizing the Training Schedule of Multilingual NMT using Reinforcement Learningcited by 0
  31. 2026Leveraging Audio-LLMs to Filter Speech-to-Speech Training Datacited by 0