4 citations · 8 across the 3 of their papers we have counts for
4 papers · 1 filter
MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition
David Ifeoluwa Adelani, Graham Neubig, Sebastian Ruder +42
African languages are spoken by over a billion people, but are underrepresented in NLP research and development. The challenges impeding progress include the limited availability o…
Domain-specific MT for Low-resource Languages: The case of Bambara-French
Allahsera Auguste Tapo, Michael Leventhal, Sarah Luger +2
Translating to and from low-resource languages is a challenge for machine translation (MT) systems due to a lack of parallel data. In this paper we address the issue of domain-spec…
Neural Machine Translation for Extremely Low-Resource African Languages: A Case Study on Bambara
Allahsera Auguste Tapo, Bakary Coulibaly, Sébastien Diarra +6
Low-resource languages present unique challenges to (neural) machine translation. We discuss the case of Bambara, a Mande language for which training data is scarce and requires si…
Assessing Human Translations from French to Bambara for Machine Learning: a Pilot Study
Michael Leventhal, Allahsera Tapo, Sarah Luger +2
We present novel methods for assessing the quality of human-translated aligned texts for learning machine translation models of under-resourced languages. Malian university student…