mlsanityData to 7 Oct 2026

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

arXiv 2501.06374 ↗First posted 10 Jan 2025cs.CLcited by 11

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Abstract (arXiv)

This paper introduces AFRIDOC-MT, a document-level multi-parallel translation dataset covering English and five African languages: Amharic, Hausa, Swahili, Yor`ub'a, and Zulu. The dataset comprises 334 health and 271 information technology news documents, all human-translated from English to these languages. We conduct document-level translation benchmark experiments by evaluating neural machine translation (NMT) models and large language models (LLMs) for translations between English and these languages, at both the sentence and pseudo-document levels. These outputs are realigned to form complete documents for evaluation. Our results indicate that NLLB-200 achieved the best average performance among the standard NMT models, while GPT-4o outperformed general-purpose LLMs. Fine-tuning selected models led to substantial performance gains, but models trained on sentences struggled to generalize effectively to longer documents. Furthermore, our analysis reveals that some LLMs exhibit issues such as under-generation, repetition of words or phrases, and off-target translations, especially for African languages.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 42 of its 86 references that are in the map. First posted on arXiv 10 Jan 2025.

Builds on

42 of 86 references.

References · 42

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30 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 8.

11 papers.

  1. 2025DocHPLT: A Massively Multilingual Document-Level Translation Datasetcited by 7
  2. 2026What Does LLM Refinement Actually Improve? A Systematic Study on Document-Level Literary Translationcited by 2
  3. 2025OpenWHO: A Document-Level Parallel Corpus for Health Translation in Low-Resource Languagescited by 1
  4. 2025Automatic Correction of Writing Anomalies in Hausa Textscited by 0
  5. 2026AmharicStoryQA: A Multicultural Story Question Answering Benchmark in Amhariccited by 0
  6. 2026AfrIFact: Cultural Information Retrieval, Evidence Extraction and Fact Checking for African Languagescited by 0
  7. 2026Mining Large Language Models for Low-Resource Language Data: Comparing Elicitation Strategies for Hausa and Fongbecited by 0
  8. 2026AfriScience-MT: Towards Decolonizing Science in Africa through Text Translationcited by 0
  9. 2026Evaluating Large Language Models for Hausa and Fongbe Machine Translation: Benchmarks, Failures, and Metric Reliabilitycited by 0
  10. 2026TranslatePsy-AfriSLM: High-Quality Data Scaling For Low-Resource Machine Translationcited by 0
  11. 2026Doc2FRC: Length-Consistent Document-Level Machine Translation via Fixed-Range Chunkingcited by 0