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Article published In:
Indirect Translation and Sustainable Development
Edited by Jan Buts, Hanna Pięta, Laura Ivaska and James Hadley
[Translation Spaces 12:2] 2023
► pp. 231–254
Special Issue Article

Theorizing sustainable, low-resource MT in development settings

Pivot-based MT between Guatemala’s indigenous Mayan languages

ORCID logo | Dublin City University

This article conducts a meta-analysis of existing research to theorize how machine translation (MT) may help resolve underlying contradictions in the development sector that preclude the UN’s 10th Sustainable Development Goal: to reduce inequality within and among countries. Non-governmental organizations (NGOs) frequently work in dominant languages and neglect marginalized languages, reinforcing power imbalances between the Global North and Global South in development planning. MT between marginalized languages may improve collaboration between local communities to redress shared disadvantages. As an example, the article hypothesizes a sustainable, “low-tech” MT system pivoting through Spanish to translate between three Mayan languages in Guatemala: K’iche’, Q’eqchi’, and Mam. First, the article theorizes three key dimensions comprising the overall sustainability of low-resource MT in development: quality, social, and environmental. It then evaluates the sustainability of various MT architectures. Finally, it reaffirms the ability for indirect translation (classic pivot-based MT) to facilitate MT between low-resource languages.

Keywords: low-resource machine translation, sustainable machine translation, translation in development, indirect translation, Mayan languages
Article outline
  • 1.Introduction
  • 2.Defining sustainable MT workflows for low-resource languages
    • 2.1Previous work on sustainable MT
    • 2.2Three sustainability dimensions for MT workflows for low-resource languages
    • 3.Evaluating the sustainability of MT architectures for low-resource languages
      • 3.1Rule-based MT
      • 3.2Statistical MT
      • 3.3Classic neural MT
      • 3.4Multilingual neural MT
      • 3.5Hybrid MT
      • 3.6Evaluation results
    • 4.Pivot-based MT as sustainable MT
  • 5.Identifying additional challenges and mitigating strategies
    • 5.1Potential training data sets
    • 5.2Insufficient training data after pivoting
    • 5.3Morphological complexity
    • 5.4Inconsistent orthography
  • 6.Limitations
  • 7.Conclusion
  • References
Published online: 10 May 2023
DOI logo
https://doi.org/10.1075/ts.22018.rie
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