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home / papers / 2026 / Diacritic Restoration for Low-Resource Indigenous Languages: Case Study with Bribri and Cook Islands Māori

Diacritic Restoration for Low-Resource Indigenous Languages: Case Study with Bribri and Cook Islands Māori

Summary

Many Indigenous languages adopted writing only recently, so diacritics are used inconsistently or left out, which hampers automatic processing. This paper addresses diacritic restoration, which means recovering marks in text where they are missing, for two severely under-resourced languages. Bribri is a Chibchan language of Costa Rica whose spelling encodes tone, nasality and vowel laxness. Cook Islands Māori (CIM) is a Polynesian language that marks long vowels with a macron and glottal stops with a saltillo.

The authors benchmark three kinds of systems: character-based translation models, fine-tuned LLMs and zero-shot LLMs. They also track how accuracy grows with training data, compare different resource conditions, and run a small study on correcting inconsistent diacritics.

Fine-tuned character-level LLMs perform best, probably because working on UTF-8 bytes lets them break composite characters into parts. Massively multilingual models fall behind, and zero-shot prompting fails throughout. Dependable output appears at about 10,000 words of training text.

The work responds to explicit community requests for tools. It also analyses errors on tones and glottal stops, and it insists that spelling variation is not a mistake to be eliminated.

Key points

  • Bribri has about 7,000 speakers and at least three orthographies (Constenla et al. 2004, Jara 2018, Margery 2005); the experiments use Constenla's. It marks tone with three diacritics plus a glottal symbol, nasality with an underline and lax vowels with an umlaut. A single word, taî 'much', has been found spelled 14 different ways.
  • CIM has roughly 12,500 speakers in the Cook Islands and about 10,000 in the diaspora. The CIMR orthography (Nicholas 2017) uses the saltillo (U+A78C), which in real text is often replaced by an apostrophe (U+0027), a left quote (U+201C) or an ʻokina (U+02BB).
  • Main findings:
    • Fine-tuned character/byte-level LLMs come out on top.
    • Massively multilingual models underperform with this little data.
    • Zero-shot LLMs perform poorly in every setting.
    • Roughly 10,000 words of data is where reliable restoration begins.
  • Prior work on the related te reo Māori relied on much larger corpora. Cocks et al. (2011) reached 99% accuracy with a Naïve Bayes classifier on 3.8M words, and Nakano (2023) reached 96% with an RNN on 463k words.
  • Ethical stance: the authors frame normalization as the job of tool developers, not language users. They warn against promoting any single spelling variant as the de facto norm.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.63317/4c9samfkamqy

@inproceedings{cotosolano2026diacritic,
  title = {{Diacritic Restoration for Low-Resource Indigenous Languages: Case Study with Bribri and Cook Islands Māori}},
  author = {Coto-Solano, Rolando and Li, Daisy and Ferraz, Manoela Teleginski and Sasse, Olivia and Krupka, Cha and Loaiciga, Sharid and Nicholas, Sally Akevai Tenamu},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/4c9samfkamqy},
  url = {https://doi.org/10.63317/4c9samfkamqy}
}

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