The translation of minority languages with artificial intelligence: promise, pitfalls and best practices

Artificial intelligence (AI) has dramatically accelerated progress in machine translation. However, outside the major “well-resourced” languages (English, French, Spanish, etc.), results can be inconsistent, or even deceptively convincing. This is particularly true for minority and regional languages, which often have limited corpora, multiple orthographic variations and fewer standardised references.

In this article, we explore AI-powered minority language translation: what AI can realistically achieve, the challenges involved, where the risks of errors lie, and how to combine digital tools with human expertise to avoid mistranslations.

Why AI still struggles with minority languages

Machine translation systems and large language models primarily rely on the quantity and quality of available data. When a language benefits from extensive corpora, edited content, digital dictionaries, parallel translations and standardised usages, the results are often fairly accurate. By contrast, for minority languages, AI often has to work with insufficient, fragmented or heterogeneous data.

The first problem is quantitative: lesser-used languages rarely have large, high-quality corpora. AI must therefore extrapolate from incomplete data, resulting in seemingly plausible yet fundamentally incorrect outcomes.

The second issue concerns quality. Many regional languages include local variants, non-standardised usages, predominantly oral forms and cultural references that are difficult to standardise. As a result, AI struggles to handle elements that depend on community heritage, historical context or living memory.

A benchmark reported by Slator across 79 languages shows precisely that detection and analysis systems perform less effectively in low-resource languages, and that content translated or rewritten by AI further complicates the evaluation of results.

Guernésiais: one example among many

Guernésiais provides a clear example of these difficulties. Historically rooted in Guernsey, this regional language is now considered vulnerable, with a declining number of speakers and increasingly difficult transmission.
This situation is partly explained by the dominance of English in daily life and a decline in intergenerational transmission. It is further exacerbated by the lack of linguistic resources (corpora, digital content), which limits both learning opportunities and integration into translation tools. Guernésiais is currently taught only optionally in certain schools, often outside the main curriculum, contributing to its gradual marginalisation.

When linguistic resources are insufficient, the tools required for reliable translation —corpora, dictionaries and validated terminology— are lacking. Creating and structuring these resources therefore becomes a major challenge, both for human translators and for AI systems. In this context, translation can no longer rely solely on automated or standardised processes.

When a language has only a limited number of speakers left, often older generations, each error in translation, teaching or transmission can further weaken this linguistic heritage.
Without efforts in documentation, teaching and promotion, the risk is twofold: a gradual loss of the language and an inability to produce reliable translations.
The preservation of these languages therefore depends heavily on the commitment of local communities, researchers and cultural institutions, as well as on the availability of strong linguistic resources to ensure transmission to future generations.

Risks of errors in AI translation: where things often go wrong

A tool may produce a fluent sentence that is still incorrect from a lexical, grammatical or cultural perspective. And the rarer the language, the more likely the user is to miss the error.

AI may invent a non-existent form, wrongly associate a minority language with a neighbouring dominant language, or recreate phrasing based on patterns learned from related languages. This results in text that appears credible, but lacks authenticity.

Minority languages often convey nuances related to territory, social practices and collective memory. Automated translation can erase these layers of meaning, or even replace a local expression with a modern equivalent that is not an exact match.

This issue is not limited to heritage languages. In March 2026, an article by PCWorld showed that AI-assisted translations on Wikipedia were introducing factual errors, including incorrect citations, swapped or unrelated sources and passages that were not supported by the referenced material. The impact of these issues is all the more concerning as the content involved reaches a very large audience. According to estimates published by Analyzify, Wikipedia generated approximately 132 billion page views in 2025, with nearly 11 billion visits in January alone.

This reality serves as a reminder that machine-generated translation should never be considered inherently reliable, especially when it concerns cultural, historical, terminological or educational content.

Digital tools for regional languages: can they be used and what for?

Yes, provided that technological assistance is not confused with blind automation. When used wisely, technology can play a positive role in preserving minority languages.

Useful and realistic uses

• Digitisation of dictionaries and archives
• Creation of glossaries and terminological databases
• Development of learning applications
• Corpus annotation with the help of speakers and specialists
• Dissemination of audio, educational and intergenerational content
• Highlighting place names, surnames and traditional expressions

The official support programme in Guernsey also establishes clear priorities: teaching, revitalisation, research, archiving, awareness-raising and development of digital resources. These initiatives are part of the language policy led by the Guernsey Language Commission.

By contrast, using AI as an automatic substitute for expert speakers or specialist translators can propagate fragile, approximate or outright incorrect forms.

A hybrid approach for publishing with confidence

For a publication designed to last (website, brochure, signage), the hybrid approach is the most effective: AI to accelerate production, humans to ensure meaning, style and cultural fidelity.

This is precisely the advantage of working with a translation agency that can coordinate the process without sacrificing quality. At AbroadLink Translations, we combine AI tools, quality control and specialised translators to ensure the quality of your content, even in the case of rare or heavily dialectal languages.

Delving deeper into the role of AI in multilingual workflows

To delve deeper into the role of AI in multilingual workflows, you can also consult our analysis on the future of translation in the AI era. AbroadLink Translations supports your projects in the information technology sector by combining advanced technologies and expert human validation to ensure reliable, consistent translations tailored to technical environments and end-users. AbroadLink Translations also operates in the medical and medical device sector, ensuring translations that comply with regulatory requirements, validated by experts, and adapted to patient safety issues.

Conclusion

AI translation opens new perspectives for knowledge dissemination and multilingual content production. However, when applied to minority languages, its limitations soon become apparent. With a limited number of speakers and scarce digital resources, automated systems often lack the data necessary to produce reliable translations. Uncontrolled use of these tools can therefore generate linguistic, cultural or terminological errors. Nevertheless, technology can also play a positive role, particularly in the digitisation of archives, the creation of educational resources and the dissemination of educational content. The challenge is therefore to strike a balance between technological innovation and human expertise. Validation by specialists and native speakers remains essential to ensure the quality and authenticity of translations. In this context, the combination of digital tools and strong linguistic expertise appears to be the best approach for preserving and enhancing minority languages.

This quality requirement is all the more crucial in an environment where highly consulted platforms, such as Wikipedia, may contain errors, poorly sourced content or unreliable information. It is, indeed, a collaborative site that any user can edit, even though control mechanisms exist. Given the massive audience of such platforms, these inaccuracies can spread on a large scale and influence users' understanding in the long term. More broadly, media outlets and organisations must be extra vigilant when using machine translation. This issue is particularly sensitive in sectors such as healthcare or regulatory fields, where safety, compliance and legal responsibility are critical. In these contexts, human intervention is not optional; it is absolutely necessary to ensure the reliability and validity of translated content.

The best strategy is to invest in resources (glossaries, validated corpora), use digital tools in a controlled manner and maintain a human proofreading stage.