mlsanityData to 7 Oct 2026

Neural Machine Translation for the Indigenous Languages of the Americas: An Introduction

arXiv 2306.06804 ↗First posted 11 Jun 2023cs.CL stat.MLcited by 7

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Abstract (arXiv)

Neural models have drastically advanced state of the art for machine translation (MT) between high-resource languages. Traditionally, these models rely on large amounts of training data, but many language pairs lack these resources. However, an important part of the languages in the world do not have this amount of data. Most languages from the Americas are among them, having a limited amount of parallel and monolingual data, if any. Here, we present an introduction to the interested reader to the basic challenges, concepts, and techniques that involve the creation of MT systems for these languages. Finally, we discuss the recent advances and findings and open questions, product of an increased interest of the NLP community in these languages.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 119 of its 247 references that are in the map. First posted on arXiv 11 Jun 2023.

Builds on

119 of 247 references.

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50 most cited of 119 All 119 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 3; 2026: 1.

7 papers.

  1. 2025Crowdsource, Crawl, or Generate? Creating SEA-VL, a Multicultural Vision-Language Dataset for Southeast Asiacited by 8
  2. 2025Advancing Dialectal Arabic to Modern Standard Arabic Machine Translationcited by 6
  3. 2024NLP Progress in Indigenous Latin American Languagescited by 3
  4. 2024Harnessing the Power of Artificial Intelligence to Vitalize Endangered Indigenous Languages: Technologies and Experiencescited by 1
  5. 2024Building Efficient and Effective OpenQA Systems for Low-Resource Languagescited by 0
  6. 2025Redefining technology for indigenous languagescited by 0
  7. 2026Translation or Recitation? Calibrating Evaluation Scores for Machine Translation of Extremely Low-Resource Languagescited by 0