mlsanityData to 7 Oct 2026

Does Neural Machine Translation Benefit from Larger Context?

arXiv 1704.05135 ↗First posted 17 Apr 2017stat.ML cs.CL cs.LGcited by 38

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Abstract (arXiv)

We propose a neural machine translation architecture that models the surrounding text in addition to the source sentence. These models lead to better performance, both in terms of general translation quality and pronoun prediction, when trained on small corpora, although this improvement largely disappears when trained with a larger corpus. We also discover that attention-based neural machine translation is well suited for pronoun prediction and compares favorably with other approaches that were specifically designed for this task.

Key facts. Cited by 38 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Apr 2017.

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Citing papers per year: 2021: 15; 2022: 3; 2023: 10; 2024: 5; 2025: 4; 2026: 1.

38 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. 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. 2022Exploring Document-Level Literary Machine Translation with Parallel Paragraphs from World Literaturecited by 45
  6. 2021Do Context-Aware Translation Models Pay the Right Attention?cited by 21
  7. 2023Contextual Refinement of Translations: Large Language Models for Sentence and Document-Level Post-Editingcited by 21
  8. 2021Modeling Bilingual Conversational Characteristics for Neural Chat Translationcited by 18
  9. 2021Diverse Pretrained Context Encodings Improve Document Translationcited by 13
  10. 2021Divide and Rule: Effective Pre-Training for Context-Aware Multi-Encoder Translation Modelscited by 12
  11. 2021Towards Making the Most of Dialogue Characteristics for Neural Chat Translationcited by 12
  12. 2023Document-Level Language Models for Machine Translationcited by 12
26 more
  1. 2021Data Augmentation by Concatenation for Low-Resource Translation: A Mystery and a Solutioncited by 11
  2. 2021Contrastive Learning for Context-aware Neural Machine TranslationUsing Coreference Informationcited by 9
  3. 2021DOCmT5: Document-Level Pretraining of Multilingual Language Modelscited by 8
  4. 2023Improving Long Context Document-Level Machine Translationcited by 8
  5. 2021A Comparison of Approaches to Document-level Machine Translationcited by 7
  6. 2021Multilingual Document-Level Translation Enables Zero-Shot Transfer From Sentences to Documentscited by 7
  7. 2024DeMPT: Decoding-enhanced Multi-phase Prompt Tuning for Making LLMs Be Better Context-aware Translatorscited by 6
  8. 2025Extending Automatic Machine Translation Evaluation to Book-Length Documentscited by 6
  9. 2022P-Transformer: Towards Better Document-to-Document Neural Machine Translationcited by 5
  10. 2024Is Context Helpful for Chat Translation Evaluation?cited by 5
  11. 2023HanoiT: Enhancing Context-aware Translation via Selective Contextcited by 4
  12. 2024Analyzing the Attention Heads for Pronoun Disambiguation in Context-aware Machine Translation Modelscited by 4
  13. 2021Zero-pronoun Data Augmentation for Japanese-to-English Translationcited by 3
  14. 2023A Multi-task Multi-stage Transitional Training Framework for Neural Chat Translationcited by 3
  15. 2025You Are What You Train: Effects of Data Composition on Training Context-aware Machine Translation Modelscited by 3
  16. 2024Sequence Shortening for Context-Aware Machine Translationcited by 2
  17. 2024Context-Aware Machine Translation with Source Coreference Explanationcited by 2
  18. 2025GRAFT: A Graph-based Flow-aware Agentic Framework for Document-level Machine Translationcited by 2
  19. 2021Large-Context Conversational Representation Learning: Self-Supervised Learning for Conversational Documentscited by 1
  20. 2021Revisiting Context Choices for Context-aware Machine Translationcited by 1
  21. 2022SMDT: Selective Memory-Augmented Neural Document Translationcited by 1
  22. 2023Differentially Private Adapters for Parameter Efficient Acoustic Modelingcited by 1
  23. 2023Addressing the Length Bias Problem in Document-Level Neural Machine Translationcited by 1
  24. 2023On Search Strategies for Document-Level Neural Machine Translationcited by 0
  25. 2025Interpretable Style Takagi-Sugeno-Kang Fuzzy Clusteringcited by 0
  26. 2026Scaling Attention Head Analysis via Gradient-Based Attribution in Context-Aware Machine Translationcited by 0