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Improving Similar Language Translation With Transfer Learning

arXiv 2108.03533 ↗First posted 7 Aug 2021cs.AI cs.CLcited by 1

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Abstract (arXiv)

We investigate transfer learning based on pre-trained neural machine translation models to translate between (low-resource) similar languages. This work is part of our contribution to the WMT 2021 Similar Languages Translation Shared Task where we submitted models for different language pairs, including French-Bambara, Spanish-Catalan, and Spanish-Portuguese in both directions. Our models for Catalan-Spanish (82.79 BLEU) and Portuguese-Spanish (87.11 BLEU) rank top 1 in the official shared task evaluation, and we are the only team to submit models for the French-Bambara pairs.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 11 of its 23 references that are in the map. First posted on arXiv 7 Aug 2021.

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11 of 23 references.

References · 11

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Beyond English-Centric Multilingual Machine Translationcited by 559
  3. 2019Assessing BERT's Syntactic Abilitiescited by 191
  4. 2016Transfer Learning for Low-Resource Neural Machine Translationcited by 188
  5. 2020Language Model Prior for Low-Resource Neural Machine Translationcited by 37
  6. 2021How transfer learning impacts linguistic knowledge in deep NLP models?cited by 32
  7. 2019Acquiring Knowledge from Pre-trained Model to Neural Machine Translationcited by 30
  8. 2021Exploring Text-to-Text Transformers for English to Hinglish Machine Translation with Synthetic Code-Mixingcited by 16
  9. 2021On the Copying Behaviors of Pre-Training for Neural Machine Translationcited by 15
  10. 2020Translating Similar Languages: Role of Mutual Intelligibility in Multilingual Transformerscited by 6
  11. 2021Translating the Unseen? Yoruba-English MT in Low-Resource, Morphologically-Unmarked Settingscited by 5

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  1. 2024From Brazilian Portuguese to European Portuguesecited by 0