Volume 47, Issue 1, November 2025, Pages 24–30
Béthel C. A. R. K. Atohoun1, Jean-Paul Tognon2, and Mikaël A. Mousse3
1 Ecole Supérieur de Gestion d’Information et des Sciences, Cotonou, Bénin
2 Institut Universitaire de Technologie, Université de Parakou, Parakou, Bénin
3 Institut Universitaire de Technologie, Université de Parakou, Parakou, Benin
Original language: English
Copyright © 2025 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
This paper presents a bidirectional neural machine translation system between French and Fon, a major language spoken in Benin and belonging to the Gbe family. Unlike existing generic translation tools, our system is specifically designed to address the linguistic, cultural, and computational challenges of low-resource African languages. The proposed architecture builds upon Facebook AI’s NLLB-200 model, which we adapt through cross-linguistic transfer from Ewe to Fon, taking advantage of structural similarities within the Gbe languages. To further enhance performance, we employ the T-Projection method for more reliable annotation and integrate a continuous improvement framework driven by real-time user feedback. Evaluation was conducted on a 73MB French–Fon parallel corpus. The results indicate a 25% improvement in the translation of idiomatic expressions, as well as a 40% reduction in inference time through knowledge distillation. Beyond linguistic accuracy, the system introduces a cultural evaluation module, enabling context-aware translation in domains such as Vodoun practices, royal discourse, and traditional expressions. This ensures not only linguistic fidelity but also cultural adequacy. The system achieves a BLEU score of 0.94 and a user satisfaction rate of 0.93, confirming its effectiveness and relevance for real-world use in Beninese contexts.
Author Keywords: Neural Machine Translation, French–Fon, Low-Resource Languages, Cross-Linguistic Transfer, Cultural Adaptation.
Béthel C. A. R. K. Atohoun1, Jean-Paul Tognon2, and Mikaël A. Mousse3
1 Ecole Supérieur de Gestion d’Information et des Sciences, Cotonou, Bénin
2 Institut Universitaire de Technologie, Université de Parakou, Parakou, Bénin
3 Institut Universitaire de Technologie, Université de Parakou, Parakou, Benin
Original language: English
Copyright © 2025 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
This paper presents a bidirectional neural machine translation system between French and Fon, a major language spoken in Benin and belonging to the Gbe family. Unlike existing generic translation tools, our system is specifically designed to address the linguistic, cultural, and computational challenges of low-resource African languages. The proposed architecture builds upon Facebook AI’s NLLB-200 model, which we adapt through cross-linguistic transfer from Ewe to Fon, taking advantage of structural similarities within the Gbe languages. To further enhance performance, we employ the T-Projection method for more reliable annotation and integrate a continuous improvement framework driven by real-time user feedback. Evaluation was conducted on a 73MB French–Fon parallel corpus. The results indicate a 25% improvement in the translation of idiomatic expressions, as well as a 40% reduction in inference time through knowledge distillation. Beyond linguistic accuracy, the system introduces a cultural evaluation module, enabling context-aware translation in domains such as Vodoun practices, royal discourse, and traditional expressions. This ensures not only linguistic fidelity but also cultural adequacy. The system achieves a BLEU score of 0.94 and a user satisfaction rate of 0.93, confirming its effectiveness and relevance for real-world use in Beninese contexts.
Author Keywords: Neural Machine Translation, French–Fon, Low-Resource Languages, Cross-Linguistic Transfer, Cultural Adaptation.
How to Cite this Article
Béthel C. A. R. K. Atohoun, Jean-Paul Tognon, and Mikaël A. Mousse, “Advancing French-Fon Neural Translation System through Cross-Linguistic Transfer and Continuous Improvement,” International Journal of Innovation and Applied Studies, vol. 47, no. 1, pp. 24–30, November 2025.
