MudawanSn: A Gold-Standard Wolof-Arabic Parallel Corpus for Machine Translation
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Abstract (arXiv)
We present MudawanSn, a gold-standard resource of 1,271 sentence-aligned pairs manually translated from Wolof into Modern Standard Arabic (MSA). The source texts are drawn from the MasakhaNER corpus and cover politics, society, religion, and sports in Senegalese news discourse. Although multilingual resources such as FLORES-200 and NTREX include both Wolof and Arabic, no publicly available parallel corpus is specifically designed for the Wolof-Modern Standard Arabic language pair. We describe the corpus construction protocol, sentence alignment procedure, and quality-control workflow. We benchmark four machine translation systems spanning three architectural families: NLLB-200 (600M), mT5-base, and two AfriNLLB variants, showing that fine-tuning on MudawanSn yields substantial improvements in both translation directions. The best-performing model, AfriNLLB-12, achieves 7.76 BLEU and 30.72 chrF++ for Wolof-to-Arabic, and 8.75 BLEU and 33.08 chrF++ for Arabic-to-Wolof. The corpus is released under the CC BY-NC license and is publicly available on Hugging Face and GitHub.
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References · 6
- 2020mT5: A massively multilingual pre-trained text-to-text transformercited by 1,818
- 2018A Call for Clarity in Reporting BLEU Scorescited by 1,799
- 2020Beyond English-Centric Multilingual Machine Translationcited by 561
- 2023MADLAD-400: A Multilingual And Document-Level Large Audited Datasetcited by 181
- 2023Low-Resourced Machine Translation for Senegalese Wolof Languagecited by 5
- 2026AfriNLLB: Efficient Translation Models for African Languagescited by 2
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