mlsanityData to 7 Oct 2026

When and Why is Document-level Context Useful in Neural Machine Translation?

arXiv 1910.00294 ↗First posted 1 Oct 2019cs.CLcited by 45

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Abstract (arXiv)

Document-level context has received lots of attention for compensating neural machine translation (NMT) of isolated sentences. However, recent advances in document-level NMT focus on sophisticated integration of the context, explaining its improvement with only a few selected examples or targeted test sets. We extensively quantify the causes of improvements by a document-level model in general test sets, clarifying the limit of the usefulness of document-level context in NMT. We show that most of the improvements are not interpretable as utilizing the context. We also show that a minimal encoding is sufficient for the context modeling and very long context is not helpful for NMT.

Key facts. Cited by 45 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Oct 2019.

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Citing papers per year: 2021: 12; 2022: 5; 2023: 13; 2024: 4; 2025: 7; 2026: 4.

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  1. 2021Gender Bias in Machine Translationcited by 116
  2. 2022Embarrassingly Easy Document-Level MT Metrics: How to Convert Any Pretrained Metric Into a Document-Level Metriccited by 56
  3. 2021Measuring and Increasing Context Usage in Context-Aware Machine Translationcited by 48
  4. 2021Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Surveycited by 46
  5. 2023Quantifying Context Mixing in Transformerscited by 43
  6. 2022MT-GenEval: A Counterfactual and Contextual Dataset for Evaluating Gender Accuracy in Machine Translationcited by 38
  7. 2021When Does Translation Require Context? A Data-driven, Multilingual Explorationcited by 36
  8. 2024DelTA: An Online Document-Level Translation Agent Based on Multi-Level Memorycited by 27
  9. 2023Gender Neutralization for an Inclusive Machine Translation: from Theoretical Foundations to Open Challengescited by 22
  10. 2021Do Context-Aware Translation Models Pay the Right Attention?cited by 21
  11. 2023A Tale of Pronouns: Interpretability Informs Gender Bias Mitigation for Fairer Instruction-Tuned Machine Translationcited by 19
  12. 2024How "Real" is Your Real-Time Simultaneous Speech-to-Text Translation System?cited by 17
33 more
  1. 2021Diverse Pretrained Context Encodings Improve Document Translationcited by 13
  2. 2021Divide and Rule: Effective Pre-Training for Context-Aware Multi-Encoder Translation Modelscited by 12
  3. 2021Data Augmentation by Concatenation for Low-Resource Translation: A Mystery and a Solutioncited by 11
  4. 2025Learning to Attribute with Attentioncited by 11
  5. 2021Contrastive Learning for Context-aware Neural Machine TranslationUsing Coreference Informationcited by 9
  6. 2024On Measuring Context Utilization in Document-Level MT Systemscited by 9
  7. 2023Improving Long Context Document-Level Machine Translationcited by 8
  8. 2021Multilingual Document-Level Translation Enables Zero-Shot Transfer From Sentences to Documentscited by 7
  9. 2022Focused Concatenation for Context-Aware Neural Machine Translationcited by 7
  10. 2025Are We Paying Attention to Her? Investigating Gender Disambiguation and Attention in Machine Translationcited by 7
  11. 2025DocHPLT: A Massively Multilingual Document-Level Translation Datasetcited by 7
  12. 2023Encoding Sentence Position in Context-Aware Neural Machine Translation with Concatenationcited by 6
  13. 2023FRASIMED: a Clinical French Annotated Resource Produced through Crosslingual BERT-Based Annotation Projectioncited by 6
  14. 2023Quantifying the Plausibility of Context Reliance in Neural Machine Translationcited by 6
  15. 2022Dodging the Data Bottleneck: Automatic Subtitling with Automatically Segmented ST Corporacited by 5
  16. 2023Challenges in Context-Aware Neural Machine Translationcited by 5
  17. 2023TRAMS: Training-free Memory Selection for Long-range Language Modelingcited by 5
  18. 2021Towards Personalised and Document-level Machine Translation of Dialoguecited by 4
  19. 2024A Case Study on Context-Aware Neural Machine Translation with Multi-Task Learningcited by 4
  20. 2022Modeling Context With Linear Attention for Scalable Document-Level Translationcited by 3
  21. 2023Long-form Simultaneous Speech Translation: Thesis Proposalcited by 3
  22. 2023Improving Language Model Integration for Neural Machine Translationcited by 2
  23. 2025Align-then-Slide: A complete evaluation framework for Ultra-Long Document-Level Machine Translationcited by 2
  24. 2021Revisiting Context Choices for Context-aware Machine Translationcited by 1
  25. 2023A Case Study on Context Encoding in Multi-Encoder based Document-Level Neural Machine Translationcited by 1
  26. 2025Automatic Evaluation Metrics for Document-level Translation: Overview, Challenges and Trendscited by 1
  27. 2025Discourse Graph Guided Document Translation with Large Language Modelscited by 1
  28. 2026Gender Disambiguation in Machine Translation: Diagnostic Evaluation in Decoder-Only Architecturescited by 1
  29. 2026Which Tokens Need Context? A Reference-Based Analysis of Translation Responsibility Using Fertility and Entropycited by 1
  30. 2023On Search Strategies for Document-Level Neural Machine Translationcited by 0
  31. 2025Beyond the Sentence: A Survey on Context-Aware Machine Translation with Large Language Modelscited by 0
  32. 2026Loong: A Human-Like Long Document Translation Agent with Observe-and-Act Adaptive Context Selectioncited by 0
  33. 2026Doc2FRC: Length-Consistent Document-Level Machine Translation via Fixed-Range Chunkingcited by 0