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Achieving Human Parity on Automatic Chinese to English News Translation

arXiv 1803.05567 ↗First posted 15 Mar 2018cs.CLcited by 119

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Abstract (arXiv)

Machine translation has made rapid advances in recent years. Millions of people are using it today in online translation systems and mobile applications in order to communicate across language barriers. The question naturally arises whether such systems can approach or achieve parity with human translations. In this paper, we first address the problem of how to define and accurately measure human parity in translation. We then describe Microsoft's machine translation system and measure the quality of its translations on the widely used WMT 2017 news translation task from Chinese to English. We find that our latest neural machine translation system has reached a new state-of-the-art, and that the translation quality is at human parity when compared to professional human translations. We also find that it significantly exceeds the quality of crowd-sourced non-professional translations.

Key facts. Cited by 119 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Mar 2018.

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Citing papers per year: 2021: 42; 2022: 19; 2023: 24; 2024: 18; 2025: 8; 2026: 8.

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  1. 2021Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translationcited by 333
  2. 2022A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translationcited by 97
  3. 2021Finetuning Pretrained Transformers into RNNscited by 94
  4. 2021Survey of Low-Resource Machine Translationcited by 92
  5. 2021BlonDe: An Automatic Evaluation Metric for Document-level Machine Translationcited by 58
  6. 2021Adaptive Nearest Neighbor Machine Translationcited by 58
  7. 2022Democratizing Neural Machine Translation with OPUS-MTcited by 57
  8. 2023A Study on ReLU and Softmax in Transformercited by 53
  9. 2021Perturbation CheckLists for Evaluating NLG Evaluation Metricscited by 49
  10. 2021Measuring and Increasing Context Usage in Context-Aware Machine Translationcited by 48
  11. 2021Learning Language Specific Sub-network for Multilingual Machine Translationcited by 48
  12. 2023HyPoradise: An Open Baseline for Generative Speech Recognition with Large Language Modelscited by 41
38 more
  1. 2021The Dangers of Underclaiming: Reasons for Caution When Reporting How NLP Systems Failcited by 38
  2. 2022MT-GenEval: A Counterfactual and Contextual Dataset for Evaluating Gender Accuracy in Machine Translationcited by 38
  3. 2021On Compositional Generalization of Neural Machine Translationcited by 34
  4. 2021Rejuvenating Low-Frequency Words: Making the Most of Parallel Data in Non-Autoregressive Translationcited by 34
  5. 2021Sequence Length is a Domain: Length-based Overfitting in Transformer Modelscited by 34
  6. 2021Bidimensional Leaderboards: Generate and Evaluate Language Hand in Handcited by 32
  7. 2021Survey on reinforcement learning for language processingcited by 31
  8. 2022On the Learning of Non-Autoregressive Transformerscited by 27
  9. 2021Reliability Testing for Natural Language Processing Systemscited by 25
  10. 2022CoCoA-MT: A Dataset and Benchmark for Contrastive Controlled MT with Application to Formalitycited by 24
  11. 2021Non-Parametric Unsupervised Domain Adaptation for Neural Machine Translationcited by 20
  12. 2024GPT-4 vs. Human Translators: A Comprehensive Evaluation of Translation Quality Across Languages, Domains, and Expertise Levelscited by 20
  13. 2021Modeling Bilingual Conversational Characteristics for Neural Chat Translationcited by 18
  14. 2021Modeling Concentrated Cross-Attention for Neural Machine Translation with Gaussian Mixture Modelcited by 18
  15. 2021Self-Training Sampling with Monolingual Data Uncertainty for Neural Machine Translationcited by 17
  16. 2021Improving Neural Machine Translation by Bidirectional Trainingcited by 17
  17. 2024Lost in the Source Language: How Large Language Models Evaluate the Quality of Machine Translationcited by 16
  18. 2024T3: Transparent Tracking & Triggering for Fine-grained Overlap of Compute & Collectivescited by 15
  19. 2023Deliberate then Generate: Enhanced Prompting Framework for Text Generationcited by 13
  20. 2024Advancing Translation Preference Modeling with RLHF: A Step Towards Cost-Effective Solutioncited by 13
  21. 2021Towards Making the Most of Dialogue Characteristics for Neural Chat Translationcited by 12
  22. 2022Automatic Song Translation for Tonal Languagescited by 12
  23. 2023Simple and Scalable Nearest Neighbor Machine Translationcited by 12
  24. 2023Document-Level Language Models for Machine Translationcited by 12
  25. 2024Machine Translation with Large Language Models: Prompt Engineering for Persian, English, and Russian Directionscited by 12
  26. 2022Data Selection Curriculum for Neural Machine Translationcited by 11
  27. 2022A baseline revisited: Pushing the limits of multi-segment models for context-aware translationcited by 11
  28. 2025Lost in Literalism: How Supervised Training Shapes Translationese in LLMscited by 11
  29. 2021Backtranslation Feedback Improves User Confidence in MT, Not Qualitycited by 10
  30. 2021Self-Guided Curriculum Learning for Neural Machine Translationcited by 9
  31. 2024Benchmarking GPT-4 against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levelscited by 9
  32. 2021Accelerating Deep Learning Inference via Learned Cachescited by 8
  33. 2021An Approach to Improve Robustness of NLP Systems against ASR Errorscited by 8
  34. 2021Guiding Teacher Forcing with Seer Forcing for Neural Machine Translationcited by 8
  35. 2021On the Difficulty of Translating Free-Order Case-Marking Languagescited by 8
  36. 2021Non-Parametric Online Learning from Human Feedback for Neural Machine Translationcited by 8
  37. 2021Rethinking Self-Supervision Objectives for Generalizable Coherence Modelingcited by 8
  38. 2023Regeneration Learning: A Learning Paradigm for Data Generationcited by 8

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