mlsanityData to 14 Oct 2026

Neural Machine Translation with Pivot Languages

arXiv 1611.04928 ↗First posted 15 Nov 2016cs.CLcited by 25

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Abstract (arXiv)

While recent neural machine translation approaches have delivered state-of-the-art performance for resource-rich language pairs, they suffer from the data scarcity problem for resource-scarce language pairs. Although this problem can be alleviated by exploiting a pivot language to bridge the source and target languages, the source-to-pivot and pivot-to-target translation models are usually independently trained. In this work, we introduce a joint training algorithm for pivot-based neural machine translation. We propose three methods to connect the two models and enable them to interact with each other during training. Experiments on Europarl and WMT corpora show that joint training of source-to-pivot and pivot-to-target models leads to significant improvements over independent training across various languages.

Key facts. Cited by 25 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 Nov 2016.

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Citing papers per year: 2021: 8; 2022: 4; 2023: 6; 2024: 3; 2025: 3; 2026: 1.

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  1. 2021Neural Machine Translation for Low-Resource Languages: A Surveycited by 109
  2. 2023A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?cited by 84
  3. 2021Revisiting the Primacy of English in Zero-shot Cross-lingual Transfercited by 47
  4. 2021A Survey on Low-Resource Neural Machine Translationcited by 16
  5. 2025Scaling Low-Resource MT via Synthetic Data Generation with LLMscited by 14
  6. 2022UM4: Unified Multilingual Multiple Teacher-Student Model for Zero-Resource Neural Machine Translationcited by 10
  7. 2023Improving Zero-shot Multilingual Neural Machine Translation by Leveraging Cross-lingual Consistency Regularizationcited by 10
  8. 2022MulZDG: Multilingual Code-Switching Framework for Zero-shot Dialogue Generationcited by 7
  9. 2023Neural Machine Translation for the Indigenous Languages of the Americas: An Introductioncited by 7
  10. 2021Simultaneous Multi-Pivot Neural Machine Translationcited by 6
  11. 2023Investigating Multi-Pivot Ensembling with Massively Multilingual Machine Translation Modelscited by 5
  12. 2021AUGVIC: Exploiting BiText Vicinity for Low-Resource NMTcited by 4
13 more
  1. 2022Improving Zero-Shot Multilingual Translation with Universal Representations and Cross-Mappingscited by 4
  2. 2021An animated picture says at least a thousand words: Selecting Gif-based Replies in Multimodal Dialogcited by 3
  3. 2022Sub-Word Alignment Is Still Useful: A Vest-Pocket Method for Enhancing Low-Resource Machine Translationcited by 3
  4. 2025Data Augmentation With Back translation for Low Resource languages: A case of English and Lugandacited by 3
  5. 2024Improving Multilingual Neural Machine Translation by Utilizing Semantic and Linguistic Featurescited by 2
  6. 2024Exploiting Domain-Specific Parallel Data on Multilingual Language Models for Low-resource Language Translationcited by 2
  7. 2026KD4MT: A Survey of Knowledge Distillation for Machine Translationcited by 2
  8. 2024ChatZero:Zero-shot Cross-Lingual Dialogue Generation via Pseudo-Target Languagecited by 1
  9. 2025Multilingual Non-Autoregressive Machine Translation without Knowledge Distillationcited by 1
  10. 2021Integrated Training for Sequence-to-Sequence Models Using Non-Autoregressive Transformercited by 0
  11. 2021Towards Reinforcement Learning for Pivot-based Neural Machine Translation with Non-autoregressive Transformercited by 0
  12. 2023End-to-end Training and Decoding for Pivot-based Cascaded Translation Modelcited by 0
  13. 2023Direct Neural Machine Translation with Task-level Mixture of Experts modelscited by 0