Findings from the Bambara - French Machine Translation Competition (BFMT 2023)

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Abstract

Orange Silicon Valley hosted a low-resource machine translation (MT) competition with monetary prizes. The goals of the competition were to raise awareness of the challenges in the low-resource MT domain, improve MT algorithms and data strategies, and support MT expertise development in the regions where people speak Bambara and other low-resource languages. The participants built Bambara to French and French to Bambara machine translation systems using data provided by the organizers and additional data resources shared amongst the competitors. This paper details each team’s different approaches and motivation for ongoing work in Bambara and the broader low-resource machine translation domain.

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APA

Da Silva, N. A., Ajayi, T. O., Antonov, A., Kamate, P. A., Coulibaly, M., Del Rio, M., … Luger, S. (2023). Findings from the Bambara - French Machine Translation Competition (BFMT 2023). In 6th Workshop on Technologies for Machine Translation of Low-Resource Languages, LoResMT 2023 - Proceedings (pp. 110–122). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.loresmt-1.9

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