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AFRIDOC-MT: Document-level MT Corpus for African Languages

Paper · 2025 · Jesujoba Oluwadara Alabi, Israel Abebe Azime, Miaoran Zhang et al.

Summary

Most machine translation research for African languages has worked on isolated sentences, and there has been almost no benchmark for translating whole documents. AFRIDOC-MT fills this gap. It is a multi-way parallel, document-level corpus pairing English with Amharic, Hausa, Swahili, Yorùbá, and Zulu. The authors collected English health articles from the WHO and technology news from Techpoint Africa. Professional translators rendered them sentence by sentence while seeing the full document. Low-scoring outputs were flagged with AfriCOMET quality estimation and then reviewed by hand.

The paper benchmarks encoder-decoder NMT systems and decoder-only LLMs. Each model translates either single sentences or longer pseudo-document chunks, and the outputs are realigned into full documents. NLLB-200 is the strongest NMT model and GPT-4o leads among LLMs. Fine-tuning helps substantially, but models tuned on sentences transfer poorly to longer inputs. Human evaluation and qualitative review agree that translating sentence by sentence currently produces better documents in African languages. GPT-4o proved unreliable as a judge for those target languages. The resource enables discourse-aware MT research for underserved languages.

Key points

  • The corpus has 334 health and 271 tech documents, about 10k sentences per domain. Health is split 240/33/61 and tech 187/25/59 across train/dev/test.
  • Four expert translators worked on each language. Translations with an AfriCOMET QE score under 0.65 were re-checked with the language coordinators.
  • The multi-way design supports 30 translation directions. NLLB-200 was fine-tuned and is reported on all of them, while most analysis covers English↔African pairs.
  • A long-context QE model (ModernBERT-base-long-context-qe-v1) gave scores that barely distinguished the systems. Evaluation therefore added GPT-4o-as-judge, human assessment (no Zulu annotators were found), and qualitative analysis.
  • When translating into African languages, LLMs often under- or over-generate, repeat words and phrases, and produce off-target output.

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

 Institutions

🎓University of CambridgeCambridge · United Kingdom243 works · 121,396 citations🎓Saarland UniversitySaarbrücken · Germany148 works · 18,287 citations🔬Institut national de recherche en sciences et technologies du numériqueLe Chesnay-Rocquencourt · France84 works · 25,414 citations🎓McGill UniversityMontreal · Canada87 works · 11,027 citations🔬German Research Centre for Artificial IntelligenceKaiserslautern · Germany77 works · 29,479 citations🎓Imperial College LondonLondon · United Kingdom48 works · 12,181 citations🔬Canadian Institute for Advanced ResearchToronto · Canada28 works · 16,725 citations🎓Sorbonne UniversitéParis · France32 works · 7,270 citations🔬National Institute of InformaticsTokyo · Japan37 works · 3,247 citations🔬ALMANACH: Modélisation et analyse linguistique automatique et humanités computationnellesParis · France19 works · 6,924 citations🔬Barcelona Supercomputing CenterBarcelona · Spain14 works · 53 citations🏢Cambridge University PressCambridge · United Kingdom5 works · 355 citations🎓African Institute for Mathematical SciencesCape Town · South Africa8 works · 797 citations🎓University of IbadanIbadan · Nigeria4 works · 1,055 citations🎓University of KwaZulu-NatalDurban · South Africa3 works · 15 citations🎓Loughborough UniversityLoughborough · United Kingdom2 works · 15 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 1Research subjects and languages the work is about.Machine translation · 1,087 works

Cite

Cite as Jesujoba Oluwadara Alabi, Israel Abebe Azime, Miaoran Zhang, Cristina España-Bonet, Rachel Bawden, Dawei Zhu, David Ifeoluwa Adelani, Clement Oyeleke Odoje, Idris Akinade, Iffat Maab, Davis David, Shamsuddeen Hassan Muhammad, Neo Putini, David O. Ademuyiwa, Andrew Caines and Dietrich Klakow. 2025. AFRIDOC-MT: Document-level MT Corpus for African Languages. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2025.emnlp-main.1413

@inproceedings{alabi2025afridoc,
  title = {{AFRIDOC-MT: Document-level MT Corpus for African Languages}},
  author = {Jesujoba Oluwadara Alabi, Israel Abebe Azime, Miaoran Zhang, Cristina España-Bonet, Rachel Bawden, Dawei Zhu, David Ifeoluwa Adelani, Clement Oyeleke Odoje, Idris Akinade, Iffat Maab, Davis David, Shamsuddeen Hassan Muhammad, Neo Putini, David O. Ademuyiwa, Andrew Caines and Dietrich Klakow},
  booktitle = {Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing},
  year = {2025},
  doi = {10.18653/v1/2025.emnlp-main.1413},
  url = {https://doi.org/10.18653/v1/2025.emnlp-main.1413}
}

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