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Exploiting Cross-Sentence Context for Neural Machine Translation

arXiv 1704.04347 ↗First posted 14 Apr 2017cs.CLcited by 48

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Abstract (arXiv)

In translation, considering the document as a whole can help to resolve ambiguities and inconsistencies. In this paper, we propose a cross-sentence context-aware approach and investigate the influence of historical contextual information on the performance of neural machine translation (NMT). First, this history is summarized in a hierarchical way. We then integrate the historical representation into NMT in two strategies: 1) a warm-start of encoder and decoder states, and 2) an auxiliary context source for updating decoder states. Experimental results on a large Chinese-English translation task show that our approach significantly improves upon a strong attention-based NMT system by up to +2.1 BLEU points.

Key facts. Cited by 48 papers in the map (data through 13 Oct 2026). First posted on arXiv 14 Apr 2017.

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Citing papers per year: 2021: 12; 2022: 4; 2023: 17; 2024: 10; 2025: 4; 2026: 1.

48 papers.

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  1. 2023Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defensecited by 423
  2. 2023Macaw-LLM: Multi-Modal Language Modeling with Image, Audio, Video, and Text Integrationcited by 171
  3. 2023Document-Level Machine Translation with Large Language Modelscited by 90
  4. 2023Uncertainty in Natural Language Generation: From Theory to Applicationscited by 61
  5. 2024Adapting Large Language Models for Document-Level Machine Translationcited by 59
  6. 2024(Perhaps) Beyond Human Translation: Harnessing Multi-Agent Collaboration for Translating Ultra-Long Literary Textscited by 49
  7. 2021Measuring and Increasing Context Usage in Context-Aware Machine Translationcited by 48
  8. 2021Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Surveycited by 46
  9. 2024DelTA: An Online Document-Level Translation Agent Based on Multi-Level Memorycited by 27
  10. 2023Findings of the WMT 2023 Shared Task on Discourse-Level Literary Translation: A Fresh Orb in the Cosmos of LLMscited by 25
  11. 2021Do Context-Aware Translation Models Pay the Right Attention?cited by 21
  12. 2023A Paradigm Shift: The Future of Machine Translation Lies with Large Language Modelscited by 19
36 more
  1. 2021Modeling Bilingual Conversational Characteristics for Neural Chat Translationcited by 18
  2. 2021TranSmart: A Practical Interactive Machine Translation Systemcited by 17
  3. 2024Salute the Classic: Revisiting Challenges of Machine Translation in the Age of Large Language Modelscited by 17
  4. 2024Findings of the WMT 2024 Shared Task on Discourse-Level Literary Translationcited by 15
  5. 2021Diverse Pretrained Context Encodings Improve Document Translationcited by 13
  6. 2021Hierarchical Transformer-based Large-Context End-to-end ASR with Large-Context Knowledge Distillationcited by 12
  7. 2021Towards Making the Most of Dialogue Characteristics for Neural Chat Translationcited by 12
  8. 2023Disco-Bench: A Discourse-Aware Evaluation Benchmark for Language Modellingcited by 10
  9. 2023A Survey on Zero Pronoun Translationcited by 9
  10. 2024On Measuring Context Utilization in Document-Level MT Systemscited by 9
  11. 2022Conversational Speech Recognition By Learning Conversation-level Characteristicscited by 8
  12. 2023Improving Long Context Document-Level Machine Translationcited by 8
  13. 2021A Comparison of Approaches to Document-level Machine Translationcited by 7
  14. 2024DeMPT: Decoding-enhanced Multi-phase Prompt Tuning for Making LLMs Be Better Context-aware Translatorscited by 6
  15. 2022P-Transformer: Towards Better Document-to-Document Neural Machine Translationcited by 5
  16. 2023Context-aware attention layers coupled with optimal transport domain adaptation and multimodal fusion methods for recognizing dementia from spontaneous speechcited by 5
  17. 2022A Bilingual Parallel Corpus with Discourse Annotationscited by 4
  18. 2023HanoiT: Enhancing Context-aware Translation via Selective Contextcited by 4
  19. 2023A Multi-task Multi-stage Transitional Training Framework for Neural Chat Translationcited by 3
  20. 2023Enhancing End-to-End Conversational Speech Translation Through Target Language Context Utilizationcited by 3
  21. 2023Density of States Prediction of Crystalline Materials via Prompt-guided Multi-Modal Transformercited by 3
  22. 2024Enhancing AI-based Generation of Software Exploits with Contextual Informationcited by 3
  23. 2021Transfer Learning for Sequence Generation: from Single-source to Multi-sourcecited by 2
  24. 2021IITP at WAT 2021: System description for English-Hindi Multimodal Translation Taskcited by 2
  25. 2022Can NMT Understand Me? Towards Perturbation-based Evaluation of NMT Models for Code Generationcited by 2
  26. 2024Context-Aware Machine Translation with Source Coreference Explanationcited by 2
  27. 2024A Context-aware Framework for Translation-mediated Conversationscited by 2
  28. 2025GRAFT: A Graph-based Flow-aware Agentic Framework for Document-level Machine Translationcited by 2
  29. 2021Large-Context Conversational Representation Learning: Self-Supervised Learning for Conversational Documentscited by 1
  30. 2023How Does Pretraining Improve Discourse-Aware Translation?cited by 1
  31. 2023Addressing the Length Bias Problem in Document-Level Neural Machine Translationcited by 1
  32. 2025Automatic Evaluation Metrics for Document-level Translation: Overview, Challenges and Trendscited by 1
  33. 2026Loong: A Human-Like Long Document Translation Agent with Observe-and-Act Adaptive Context Selectioncited by 1
  34. 2023On Search Strategies for Document-Level Neural Machine Translationcited by 0
  35. 2025Interpretable Style Takagi-Sugeno-Kang Fuzzy Clusteringcited by 0
  36. 2025Unlocking Latent Discourse Translation in LLMs Through Quality-Aware Decodingcited by 0