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Zero-Shot Language Transfer vs Iterative Back Translation for Unsupervised Machine Translation

arXiv 2104.00106 ↗First posted 31 Mar 2021cs.CL cs.AI cs.LGcited by 0

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Abstract (arXiv)

This work focuses on comparing different solutions for machine translation on low resource language pairs, namely, with zero-shot transfer learning and unsupervised machine translation. We discuss how the data size affects the performance of both unsupervised MT and transfer learning. Additionally we also look at how the domain of the data affects the result of unsupervised MT. The code to all the experiments performed in this project are accessible on Github.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 25 references that are in the map. First posted on arXiv 31 Mar 2021.

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References · 18

  1. 2019Cross-lingual Language Model Pretrainingcited by 1,088
  2. 2020Multilingual Denoising Pre-training for Neural Machine Translationcited by 1,028
  3. 2015Improving Neural Machine Translation Models with Monolingual Datacited by 784
  4. 2016Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translationcited by 467
  5. 2017Unsupervised Machine Translation Using Monolingual Corpora Onlycited by 272
  6. 2016Transfer Learning for Low-Resource Neural Machine Translationcited by 188
  7. 2015On Using Monolingual Corpora in Neural Machine Translationcited by 149
  8. 2020The Tatoeba Translation Challenge -- Realistic Data Sets for Low Resource and Multilingual MTcited by 127
  9. 2017The IIT Bombay English-Hindi Parallel Corpuscited by 74
  10. 2019The FLoRes Evaluation Datasets for Low-Resource Machine Translation: Nepali-English and Sinhala-Englishcited by 61
  11. 2019The Missing Ingredient in Zero-Shot Neural Machine Translationcited by 59
  12. 2017Transfer Learning across Low-Resource, Related Languages for Neural Machine Translationcited by 58
6 more
  1. 2018Trivial Transfer Learning for Low-Resource Neural Machine Translationcited by 47
  2. 2016Zero-Resource Translation with Multi-Lingual Neural Machine Translationcited by 46
  3. 2020When Does Unsupervised Machine Translation Work?cited by 38
  4. 2020A Call for More Rigor in Unsupervised Cross-lingual Learningcited by 37
  5. 2016Semi-Supervised Learning for Neural Machine Translationcited by 36
  6. 2019Unsupervised Domain Adaptation for Neural Machine Translation with Domain-Aware Feature Embeddingscited by 14

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