Findings from the Bambara - French Machine Translation Competition (BFMT 2023)
May 1, 2023·,,,,,,,,,,,,,,,,,,,,·
0 min read
Ninoh Agostinho Da Silva
Tunde Oluwaseyi Ajayi
Alexander Antonov
Panga Azazia Kamate
Moussa Coulibaly
Mason Del Rio
Yacouba Diarra
Sebastian Diarra
Chris Emezue
Joel Hamilcaro
Christopher M. Homan
Alexander Most
Joseph Mwatukange
Peter Ohue
Michael Pham
Abdoulaye Sako
Sokhar Samb
Yaya Sy
Tharindu Cyril Weerasooriya
Yacine Zahidi
Sarah Luger
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.
Type
Publication
Proceedings of the The Sixth Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2023)