mlsanityData to 14 Oct 2026

IndT5: A Text-to-Text Transformer for 10 Indigenous Languages

arXiv 2104.07483 ↗First posted 4 Apr 2021cs.CLcited by 10

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Abstract (arXiv)

Transformer language models have become fundamental components of natural language processing based pipelines. Although several Transformer models have been introduced to serve many languages, there is a shortage of models pre-trained for low-resource and Indigenous languages. In this work, we introduce IndT5, the first Transformer language model for Indigenous languages. To train IndT5, we build IndCorpus--a new dataset for ten Indigenous languages and Spanish. We also present the application of IndT5 to machine translation by investigating different approaches to translate between Spanish and the Indigenous languages as part of our contribution to the AmericasNLP 2021 Shared Task on Open Machine Translation. IndT5 and IndCorpus are publicly available for research

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 17 of its 29 references that are in the map. First posted on arXiv 4 Apr 2021.

Builds on

17 of 29 references.

References · 17

  1. 2017Attention Is All You Needcited by 60,361
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  8. 2016Transfer Learning for Low-Resource Neural Machine Translationcited by 188
  9. 2019Revisiting Low-Resource Neural Machine Translation: A Case Studycited by 61
  10. 2017Transfer Learning across Low-Resource, Related Languages for Neural Machine Translationcited by 58
  11. 2018Trivial Transfer Learning for Low-Resource Neural Machine Translationcited by 47
  12. 2020Language Model Prior for Low-Resource Neural Machine Translationcited by 37
5 more
  1. 2018Challenges of language technologies for the indigenous languages of the Americascited by 29
  2. 2019Handling Syntactic Divergence in Low-resource Machine Translationcited by 8
  3. 2020Translating Similar Languages: Role of Mutual Intelligibility in Multilingual Transformerscited by 6
  4. 2021Translating the Unseen? Yoruba-English MT in Low-Resource, Morphologically-Unmarked Settingscited by 5
  5. 2020Towards Neural Machine Translation for Edoid Languagescited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 3; 2022: 2; 2023: 2; 2024: 2; 2025: 0; 2026: 1.

10 papers.

  1. 2021The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translationcited by 560
  2. 2021AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language Processingcited by 131
  3. 2022Evaluation of Transfer Learning for Polish with a Text-to-Text Modelcited by 13
  4. 2024TeenyTinyLlama: open-source tiny language models trained in Brazilian Portuguesecited by 13
  5. 2023Neural Machine Translation for the Indigenous Languages of the Americas: An Introductioncited by 7
  6. 2022Improving Neural Machine Translation of Indigenous Languages with Multilingual Transfer Learningcited by 4
  7. 2023Parallel Corpus for Indigenous Language Translation: Spanish-Mazatec and Spanish-Mixteccited by 4
  8. 2024NLP Progress in Indigenous Latin American Languagescited by 4
  9. 2026Improving Indigenous Language Machine Translation with Synthetic Data and Language-Specific Preprocessingcited by 2
  10. 2021Machine Translation of Low-Resource Indo-European Languagescited by 1