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Neural Machine Translation with Extended Context

arXiv 1708.05943 ↗First posted 20 Aug 2017cs.CLcited by 95

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Abstract (arXiv)

We investigate the use of extended context in attention-based neural machine translation. We base our experiments on translated movie subtitles and discuss the effect of increasing the segments beyond single translation units. We study the use of extended source language context as well as bilingual context extensions. The models learn to distinguish between information from different segments and are surprisingly robust with respect to translation quality. In this pilot study, we observe interesting cross-sentential attention patterns that improve textual coherence in translation at least in some selected cases.

Key facts. Cited by 95 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Aug 2017.

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Citing papers per year: 2021: 20; 2022: 13; 2023: 27; 2024: 21; 2025: 9; 2026: 5.

95 papers.

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  1. 2023Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defensecited by 401
  2. 2023Large language models effectively leverage document-level context for literary translation, but critical errors persistcited by 80
  3. 2024Adapting Large Language Models for Document-Level Machine Translationcited by 59
  4. 2021Measuring and Increasing Context Usage in Context-Aware Machine Translationcited by 48
  5. 2022On the Blind Spots of Model-Based Evaluation Metrics for Text Generationcited by 48
  6. 2021Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Surveycited by 46
  7. 2021G-Transformer for Document-level Machine Translationcited by 46
  8. 2022Exploring Document-Level Literary Machine Translation with Parallel Paragraphs from World Literaturecited by 45
  9. 2022MT-GenEval: A Counterfactual and Contextual Dataset for Evaluating Gender Accuracy in Machine Translationcited by 38
  10. 2021When Does Translation Require Context? A Data-driven, Multilingual Explorationcited by 36
  11. 2023Escaping the sentence-level paradigm in machine translationcited by 27
  12. 2021Do Context-Aware Translation Models Pay the Right Attention?cited by 21
38 more
  1. 2023Contextual Refinement of Translations: Large Language Models for Sentence and Document-Level Post-Editingcited by 21
  2. 2021Investigating Failures of Automatic Translation in the Case of Unambiguous Gendercited by 20
  3. 2021Modeling Bilingual Conversational Characteristics for Neural Chat Translationcited by 18
  4. 2024How "Real" is Your Real-Time Simultaneous Speech-to-Text Translation System?cited by 17
  5. 2022Learn To Remember: Transformer with Recurrent Memory for Document-Level Machine Translationcited by 16
  6. 2023Document Flattening: Beyond Concatenating Context for Document-Level Neural Machine Translationcited by 15
  7. 2021Diverse Pretrained Context Encodings Improve Document Translationcited by 13
  8. 2024Enhancing Document-level Translation of Large Language Model via Translation Mixed-instructionscited by 13
  9. 2021Divide and Rule: Effective Pre-Training for Context-Aware Multi-Encoder Translation Modelscited by 12
  10. 2021Towards Making the Most of Dialogue Characteristics for Neural Chat Translationcited by 12
  11. 2023Document-Level Language Models for Machine Translationcited by 12
  12. 2025Understanding In-Context Machine Translation for Low-Resource Languages: A Case Study on Manchucited by 12
  13. 2021Data Augmentation by Concatenation for Low-Resource Translation: A Mystery and a Solutioncited by 11
  14. 2022A baseline revisited: Pushing the limits of multi-segment models for context-aware translationcited by 11
  15. 2025AFRIDOC-MT: Document-level MT Corpus for African Languagescited by 11
  16. 2021Towards User-Driven Neural Machine Translationcited by 10
  17. 2024Investigating Length Issues in Document-level Machine Translationcited by 10
  18. 2021Contrastive Learning for Context-aware Neural Machine TranslationUsing Coreference Informationcited by 9
  19. 2022From Simultaneous to Streaming Machine Translation by Leveraging Streaming Historycited by 9
  20. 2023Training and Meta-Evaluating Machine Translation Evaluation Metrics at the Paragraph Levelcited by 9
  21. 2024On Measuring Context Utilization in Document-Level MT Systemscited by 9
  22. 2021DOCmT5: Document-Level Pretraining of Multilingual Language Modelscited by 8
  23. 2023Improving Long Context Document-Level Machine Translationcited by 8
  24. 2021A Comparison of Approaches to Document-level Machine Translationcited by 7
  25. 2021Multilingual Document-Level Translation Enables Zero-Shot Transfer From Sentences to Documentscited by 7
  26. 2022Scheduled Multi-task Learning for Neural Chat Translationcited by 7
  27. 2022Focused Concatenation for Context-Aware Neural Machine Translationcited by 7
  28. 2023Discourse Centric Evaluation of Machine Translation with a Densely Annotated Parallel Corpuscited by 7
  29. 2025DocHPLT: A Massively Multilingual Document-Level Translation Datasetcited by 7
  30. 2023Encoding Sentence Position in Context-Aware Neural Machine Translation with Concatenationcited by 6
  31. 2023Quantifying the Plausibility of Context Reliance in Neural Machine Translationcited by 6
  32. 2024Recovering document annotations for sentence-level bitextcited by 6
  33. 2022P-Transformer: Towards Better Document-to-Document Neural Machine Translationcited by 5
  34. 2023Challenges in Context-Aware Neural Machine Translationcited by 5
  35. 2024Is Context Helpful for Chat Translation Evaluation?cited by 5
  36. 2021Towards Personalised and Document-level Machine Translation of Dialoguecited by 4
  37. 2023HanoiT: Enhancing Context-aware Translation via Selective Contextcited by 4
  38. 2024Importance-Aware Data Augmentation for Document-Level Neural Machine Translationcited by 4

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