mlsanityData to 7 Oct 2026

Toucan: Many-to-Many Translation for 150 African Language Pairs

arXiv 2407.04796 ↗First posted 5 Jul 2024cs.CLcited by 8

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Abstract (arXiv)

We address a notable gap in Natural Language Processing (NLP) by introducing a collection of resources designed to improve Machine Translation (MT) for low-resource languages, with a specific focus on African languages. First, we introduce two language models (LMs), Cheetah-1.2B and Cheetah-3.7B, with 1.2 billion and 3.7 billion parameters respectively. Next, we finetune the aforementioned models to create toucan, an Afrocentric machine translation model designed to support 156 African language pairs. To evaluate Toucan, we carefully develop an extensive machine translation benchmark, dubbed AfroLingu-MT, tailored for evaluating machine translation. Toucan significantly outperforms other models, showcasing its remarkable performance on MT for African languages. Finally, we train a new model, spBLEU-1K, to enhance translation evaluation metrics, covering 1K languages, including 614 African languages. This work aims to advance the field of NLP, fostering cross-cultural understanding and knowledge exchange, particularly in regions with limited language resources such as Africa. The GitHub repository for the Toucan project is available at https://github.com/UBC-NLP/Toucan.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 24 of its 48 references that are in the map. First posted on arXiv 5 Jul 2024.

Builds on

24 of 48 references.

References · 24

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12 more
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  8. 2021The Effect of Domain and Diacritics in Yor`ub'a-English Neural Machine Translationcited by 38
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Citing papers per year: 2024: 1; 2025: 5; 2026: 2.

8 papers.