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Natural Language Processing for Tigrinya: Current State and Future Directions

Summary

Tigrinya is a Semitic language with about 10 million speakers, mainly in Eritrea and Ethiopia. NLP research has largely neglected it, and until this paper nobody had written a focused overview of its computational work. The authors searched several academic databases and also included theses and technical reports. They kept original studies that treat Tigrinya in real depth. For each paper they recorded its aims, methods, data, findings and released resources, and a paper could carry more than one task label. The result covers over 50 works from 2011 to 2025 across fifteen tasks, including morphology, tagging, NER, translation, QA, speech and abuse detection.

The main finding is that the field moved from hand-written rule systems to neural models, and that each step forward followed the release of a new dataset rather than a new algorithm. The remaining obstacles are:

  • the mix of root-and-pattern and affixal morphology,
  • ambiguities in the script,
  • data drawn mostly from news,
  • a lack of shared tools.

The survey is useful as a roadmap for newcomers, as a public anthology, and as a reusable template for surveying other low-resource languages.

Key points

  • Machine translation accounts for about 30% of the studies. Most of them train on fewer than 20K sentence pairs. NLLB later added 1.4M pairs and the FLORES-200 benchmark (3K samples), and an earlier hybrid SMT system with syntactic reordering reached BLEU 32.64.

  • POS tagging grew out of the Nagaoka Tigrinya Corpus, which has 4,656 hand-annotated sentences. The best result, 95.49% accuracy, comes from fine-tuning monolingual transformer language models.

  • NER improved substantially with TiNC24, which contains more than 200K labelled entities. Fine-tuned Tigrinya language models reach an F1 of 90.18% on it.

  • The Ge'ez abugida used for Tigrinya has 249 characters. It has no letter case and does not mark consonant doubling (gemination), so words such as qäräbä ("approached") and qärräbä ("offered") are spelled identically.

  • Morphology work spans several approaches:

    • rule-based stemmers;
    • the HornMorpho finite-state transducer;
    • parser combinators;
    • LSTM models for morpheme boundaries;
    • a recent segmentation dataset.

    Newer benchmarks include TiALD, which jointly labels YouTube comments for abuse, sentiment and topic, and the Tigrinya subset of AfriHate.

Interesting ideas

  • I1. When spelling hides a pronunciation contrast that changes meaning, the paper says context must settle the reading, which argues against committing to one analysis too early. — For Mova (tokens): English heteronyms such as "lead", "record" and "read" could keep all their candidate lemmas and POS tags in the en crate until the parse or frames choose one, and the chosen reading would be logged as an explainable decision.
  • I2. Early Tigrinya SMT split words into morphemes before translating, to reduce sparsity from many rare surface forms. — For Mova (back): when translating through the meta-world into a morphologically rich target language, Mova could generate stems and affixes as separate pieces from the graph and only assemble the surface word at the end.
  • I3. TiALD labels the same comments for abuse, sentiment and topic together, so the label layers can be checked against each other. — For Mova (learn): silver data could carry labels from several layers at once (tree, frames, speech act), so that a contradiction between layers flags an item for repair or for the LLM teacher tier.
  • I4. The authors argue that large global models such as NLLB still need independent evaluation by the community. They also recommend checking bias along dialect and domain lines, not only through aggregate scores. — For Mova (learn): evaluation could report results per genre or register and per data source, so a good overall score cannot hide failures on texts outside the dominant domain.

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

Cited by

3 papers of our library cite it (of the 140884 whose bibliographies we read); 1 of them have a page here, 2 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2507.17974 · arXiv 2507.17974

@misc{gaim2025natural,
  title = {{Natural Language Processing for Tigrinya: Current State and Future Directions}},
  author = {Gaim, Fitsum and Park, Jong C.},
  year = {2025},
  doi = {10.48550/arxiv.2507.17974},
  eprint = {2507.17974},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2507.17974}
}

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