mlsanityData to 7 Oct 2026

Ethical Considerations for Machine Translation of Indigenous Languages: Giving a Voice to the Speakers

arXiv 2305.19474 ↗First posted 31 May 2023cs.CLcited by 26

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Abstract (arXiv)

In recent years machine translation has become very successful for high-resource language pairs. This has also sparked new interest in research on the automatic translation of low-resource languages, including Indigenous languages. However, the latter are deeply related to the ethnic and cultural groups that speak (or used to speak) them. The data collection, modeling and deploying machine translation systems thus result in new ethical questions that must be addressed. Motivated by this, we first survey the existing literature on ethical considerations for the documentation, translation, and general natural language processing for Indigenous languages. Afterward, we conduct and analyze an interview study to shed light on the positions of community leaders, teachers, and language activists regarding ethical concerns for the automatic translation of their languages. Our results show that the inclusion, at different degrees, of native speakers and community members is vital to performing better and more ethical research on Indigenous languages.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). Builds on 11 of its 100 references that are in the map. First posted on arXiv 31 May 2023.

Builds on

11 of 100 references.

References · 11

  1. 2017Attention Is All You Needcited by 59,932
  2. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
  3. 2017Unsupervised Machine Translation Using Monolingual Corpora Onlycited by 272
  4. 2019Evaluating Gender Bias in Machine Translationcited by 247
  5. 2022Challenges and Strategies in Cross-Cultural NLPcited by 216
  6. 2017Unsupervised Neural Machine Translationcited by 148
  7. 2019Microsoft Translator at WMT 2019: Towards Large-Scale Document-Level Neural Machine Translationcited by 56
  8. 2018Challenges of language technologies for the indigenous languages of the Americascited by 29
  9. 2022Not always about you: Prioritizing community needs when developing endangered language technologycited by 21
  10. 2022How can NLP Help Revitalize Endangered Languages? A Case Study and Roadmap for the Cherokee Languagecited by 19
  11. 2021Endangered Languages are not Low-Resourced!cited by 14

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 11; 2025: 6; 2026: 5.

26 papers.

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  1. 2023A Benchmark for Learning to Translate a New Language from One Grammar Bookcited by 74
  2. 2024Culturally Aware and Adapted NLP: A Taxonomy and a Survey of the State of the Artcited by 26
  3. 2024What Do Dialect Speakers Want? A Survey of Attitudes Towards Language Technology for German Dialectscited by 20
  4. 2023A Material Lens on Coloniality in NLPcited by 19
  5. 2024Shortcomings of LLMs for Low-Resource Translation: Retrieval and Understanding are Both the Problemcited by 17
  6. 2024"It's how you do things that matters": Attending to Process to Better Serve Indigenous Communities with Language Technologiescited by 12
  7. 2023CreoleVal: Multilingual Multitask Benchmarks for Creolescited by 9
  8. 2023Neural Machine Translation for the Indigenous Languages of the Americas: An Introductioncited by 7
  9. 2024Socially Responsible Data for Large Multilingual Language Modelscited by 7
  10. 2024Investigating Neural Machine Translation for Low-Resource Languages: Using Bavarian as a Case Studycited by 5
  11. 2024Low-resource Machine Translation: what for? who for? An observational study on a dedicated Tetun language translation servicecited by 5
  12. 2024Building Better: Avoiding Pitfalls in Developing Language Resources when Data is Scarcecited by 4
14 more
  1. 2024NLP Progress in Indigenous Latin American Languagescited by 3
  2. 2025Limitations of Religious Data and the Importance of the Target Domain: Towards Machine Translation for Guinea-Bissau Creolecited by 2
  3. 2025NLP Security and Ethics, in the Wildcited by 2
  4. 2025What Do Indonesians Really Need from Language Technology? A Nationwide Surveycited by 2
  5. 2025Beyond Weaponization: NLP Security for Medium and Lower-Resourced Languages in Their Own Rightcited by 2
  6. 2026Improving Indigenous Language Machine Translation with Synthetic Data and Language-Specific Preprocessingcited by 2
  7. 2026SAGE: Sustainable Agent-Guided Expert-tuning for Culturally Attuned Translation in Low-Resource Southeast Asiacited by 2
  8. 2024Modeling the Sacred: Considerations when Using Religious Texts in Natural Language Processingcited by 1
  9. 2024Advancing AI with Integrity: Ethical Challenges and Solutions in Neural Machine Translationcited by 0
  10. 2025Indigenous Languages Spoken in Argentina: A Survey of NLP and Speech Resourcescited by 0
  11. 2025Limited-Resource Adapters Are Regularizers, Not Linguistscited by 0
  12. 2026Task Arithmetic with Support Languages for Low-Resource ASRcited by 0
  13. 2026Meenz bleibt Meenz, but Large Language Models Do Not Speak Its Dialectcited by 0
  14. 2026Beyond Good Intentions: When Does the Framing of Multilingual and Low-Resource NLP Research Become a Caricature?cited by 0