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On the Impact of Various Types of Noise on Neural Machine Translation

arXiv 1805.12282 ↗First posted 31 May 2018cs.CLcited by 76

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Abstract (arXiv)

We examine how various types of noise in the parallel training data impact the quality of neural machine translation systems. We create five types of artificial noise and analyze how they degrade performance in neural and statistical machine translation. We find that neural models are generally more harmed by noise than statistical models. For one especially egregious type of noise they learn to just copy the input sentence.

Key facts. Cited by 76 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 May 2018.

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Citing papers per year: 2021: 21; 2022: 16; 2023: 16; 2024: 16; 2025: 6; 2026: 1.

76 papers.

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  1. 2021Dynabench: Rethinking Benchmarking in NLPcited by 447
  2. 2021Quality at a Glance: An Audit of Web-Crawled Multilingual Datasetscited by 241
  3. 2021Leveraging Automated Unit Tests for Unsupervised Code Translationcited by 82
  4. 2021Sampling-Based Approximations to Minimum Bayes Risk Decoding for Neural Machine Translationcited by 63
  5. 2021Understanding the Properties of Minimum Bayes Risk Decoding in Neural Machine Translationcited by 56
  6. 2022Data Scaling Laws in NMT: The Effect of Noise and Architecturecited by 55
  7. 2021Robust Open-Vocabulary Translation from Visual Text Representationscited by 47
  8. 2021Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Surveycited by 46
  9. 2023Mitigating Hallucinations and Off-target Machine Translation with Source-Contrastive and Language-Contrastive Decodingcited by 42
  10. 2024X-ALMA: Plug & Play Modules and Adaptive Rejection for Quality Translation at Scalecited by 36
  11. 2024A Shocking Amount of the Web is Machine Translated: Insights from Multi-Way Parallelismcited by 33
  12. 2023Dissociating language and thought in large language modelscited by 27
38 more
  1. 2022Pre-Trained Multilingual Sequence-to-Sequence Models: A Hope for Low-Resource Language Translation?cited by 24
  2. 2023There's no Data Like Better Data: Using QE Metrics for MT Data Filteringcited by 19
  3. 2023Aligning Neural Machine Translation Models: Human Feedback in Training and Inferencecited by 19
  4. 2023Make Every Example Count: On the Stability and Utility of Self-Influence for Learning from Noisy NLP Datasetscited by 18
  5. 2024Multi-News+: Cost-efficient Dataset Cleansing via LLM-based Data Annotationcited by 16
  6. 2021On the Copying Behaviors of Pre-Training for Neural Machine Translationcited by 15
  7. 2023Exploiting Biased Models to De-bias Text: A Gender-Fair Rewriting Modelcited by 14
  8. 2024Quality Does Matter: A Detailed Look at the Quality and Utility of Web-Mined Parallel Corporacited by 13
  9. 2022Data Selection Curriculum for Neural Machine Translationcited by 11
  10. 2024Critical Learning Periods: Leveraging Early Training Dynamics for Efficient Data Pruningcited by 10
  11. 2021Generating Synthetic Text Data to Evaluate Causal Inference Methodscited by 9
  12. 2021Beyond Noise: Mitigating the Impact of Fine-grained Semantic Divergences on Neural Machine Translationcited by 9
  13. 2022CipherDAug: Ciphertext based Data Augmentation for Neural Machine Translationcited by 9
  14. 2023SentAlign: Accurate and Scalable Sentence Alignmentcited by 9
  15. 2024Did Translation Models Get More Robust Without Anyone Even Noticing?cited by 9
  16. 2024A Preference-driven Paradigm for Enhanced Translation with Large Language Modelscited by 9
  17. 2024Adapters for Altering LLM Vocabularies: What Languages Benefit the Most?cited by 9
  18. 2021Gradient-guided Loss Masking for Neural Machine Translationcited by 8
  19. 2021Rethinking Data Augmentation for Low-Resource Neural Machine Translation: A Multi-Task Learning Approachcited by 8
  20. 2021Improving Arabic Diacritization by Learning to Diacritize and Translatecited by 6
  21. 2022Improving Multilingual Neural Machine Translation System for Indic Languagescited by 6
  22. 2022Simultaneous Translation for Unsegmented Input: A Sliding Window Approachcited by 6
  23. 2022Separating Grains from the Chaff: Using Data Filtering to Improve Multilingual Translation for Low-Resourced African Languagescited by 6
  24. 2023Error Norm Truncation: Robust Training in the Presence of Data Noise for Text Generation Modelscited by 6
  25. 2021Noisy UGC Translation at the Character Level: Revisiting Open-Vocabulary Capabilities and Robustness of Char-Based Modelscited by 5
  26. 2023When Vision Fails: Text Attacks Against ViT and OCRcited by 5
  27. 2023On-the-Fly Fusion of Large Language Models and Machine Translationcited by 5
  28. 2021Pushing the Right Buttons: Adversarial Evaluation of Quality Estimationcited by 4
  29. 2022Scientometric Review of Artificial Intelligence for Operations & Maintenance of Wind Turbines: The Past, Present and Futurecited by 4
  30. 2022Generating Authentic Adversarial Examples beyond Meaning-preserving with Doubly Round-trip Translationcited by 4
  31. 2024How to Learn in a Noisy World? Self-Correcting the Real-World Data Noise in Machine Translationcited by 4
  32. 2021Segmenting Subtitles for Correcting ASR Segmentation Errorscited by 3
  33. 2021BitextEdit: Automatic Bitext Editing for Improved Low-Resource Machine Translationcited by 3
  34. 2022Rare but Severe Neural Machine Translation Errors Induced by Minimal Deletion: An Empirical Study on Chinese and Englishcited by 3
  35. 2022Analyzing the Use of Influence Functions for Instance-Specific Data Filtering in Neural Machine Translationcited by 3
  36. 2023An Investigation of Noise in Morphological Inflectioncited by 3
  37. 2024Learning from Many Voices: Literary MT Using Multi-Reference Human and Synthetic Datacited by 3
  38. 2025Improving the quality of Web-mined Parallel Corpora of Low-Resource Languages using Debiasing Heuristicscited by 3

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