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Multi-Source Neural Translation

arXiv 1601.00710 ↗First posted 5 Jan 2016cs.CLcited by 37

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Abstract (arXiv)

We build a multi-source machine translation model and train it to maximize the probability of a target English string given French and German sources. Using the neural encoder-decoder framework, we explore several combination methods and report up to +4.8 Bleu increases on top of a very strong attention-based neural translation model.

Key facts. Cited by 37 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Jan 2016.

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Citing papers per year: 2021: 20; 2022: 6; 2023: 7; 2024: 3; 2025: 1.

37 papers.

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  1. 2021Neural Machine Translation for Low-Resource Languages: A Surveycited by 108
  2. 2023A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?cited by 84
  3. 2021Multilingual training for Software Engineeringcited by 43
  4. 2021Language Tags Matter for Zero-Shot Neural Machine Translationcited by 29
  5. 2022Lego-MT: Learning Detachable Models for Massively Multilingual Machine Translationcited by 21
  6. 2021A Survey on Low-Resource Neural Machine Translationcited by 16
  7. 2021Alternative Input Signals Ease Transfer in Multilingual Machine Translationcited by 14
  8. 2022An Extensible Plug-and-Play Method for Multi-Aspect Controllable Text Generationcited by 10
  9. 2024On Measuring Context Utilization in Document-Level MT Systemscited by 9
  10. 2021Adaptive Sparse Transformer for Multilingual Translationcited by 8
  11. 2024Revealing the Parallel Multilingual Learning within Large Language Modelscited by 8
  12. 2021YANMTT: Yet Another Neural Machine Translation Toolkitcited by 7
25 more
  1. 2022Multilingual Mix: Example Interpolation Improves Multilingual Neural Machine Translationcited by 7
  2. 2021Simultaneous Multi-Pivot Neural Machine Translationcited by 6
  3. 2021Parameter Differentiation based Multilingual Neural Machine Translationcited by 5
  4. 2023Investigating Multi-Pivot Ensembling with Massively Multilingual Machine Translation Modelscited by 5
  5. 2024Multiple Sources are Better Than One: Incorporating External Knowledge in Low-Resource Glossingcited by 5
  6. 2021Active Learning for Massively Parallel Translation of Constrained Text into Low Resource Languagescited by 4
  7. 2021Attentive fine-tuning of Transformers for Translation of low-resourced languages @LoResMT 2021cited by 4
  8. 2022Building Multilingual Machine Translation Systems That Serve Arbitrary X-Y Translationscited by 4
  9. 2021BERTGEN: Multi-task Generation through BERTcited by 3
  10. 2021ParCourE: A Parallel Corpus Explorer for a Massively Multilingual Corpuscited by 3
  11. 2021Revisiting Language Encoding in Learning Multilingual Representationscited by 2
  12. 2021Transfer Learning for Sequence Generation: from Single-source to Multi-sourcecited by 2
  13. 2021Neural Supervised Domain Adaptation by Augmenting Pre-trained Models with Random Unitscited by 2
  14. 2021One Source, Two Targets: Challenges and Rewards of Dual Decodingcited by 2
  15. 2022Investigation of Japanese PnG BERT language model in text-to-speech synthesis for pitch accent languagecited by 2
  16. 2023Robustness of Multi-Source MT to Transcription Errorscited by 2
  17. 2021Error-Aware Interactive Semantic Parsing of OpenStreetMapcited by 1
  18. 2021Revisiting Context Choices for Context-aware Machine Translationcited by 1
  19. 2022Informative Language Representation Learning for Massively Multilingual Neural Machine Translationcited by 1
  20. 2021Improving Zero-shot Multilingual Neural Machine Translation for Low-Resource Languagescited by 0
  21. 2023Train Global, Tailor Local: Minimalist Multilingual Translation into Endangered Languagescited by 0
  22. 2023Variable-length Neural Interlingua Representations for Zero-shot Neural Machine Translationcited by 0
  23. 2023Exploring Linguistic Similarity and Zero-Shot Learning for Multilingual Translation of Dravidian Languagescited by 0
  24. 2023Direct Neural Machine Translation with Task-level Mixture of Experts modelscited by 0
  25. 2025Contextual Cues in Machine Translation: Investigating the Potential of Multi-Source Input Strategies in LLMs and NMT Systemscited by 0