2023
EACL
EACL 2023
Findings from the Bambara - French Machine Translation Competition (BFMT 2023)
Abstract
AbstractOrange 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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Mega-Team
- 21 authors
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Interdisciplinary Bridge
- Artificial Intelligence and Natural Language Processing
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Hot Topic Early Bird
- low-resource language
Authors
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