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MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition

Research output: Contribution to Journal/MagazineJournal articlepeer-review

  • David Ifeoluwa Adelani
  • Graham Neubig
  • Sebastian Ruder
  • Shruti Rijhwani
  • Michael Beukman
  • Chester Palen-Michel
  • Constantine Lignos
  • Jesujoba O. Alabi
  • Shamsuddeen Hassan Muhammad
  • Peter Nabende
  • Cheikh M. Bamba Dione
  • Andiswa Bukula
  • Rooweither Mabuya
  • Bonaventure F. P. Dossou
  • Blessing Sibanda
  • Happy Buzaaba
  • Jonathan Mukiibi
  • Godson Kalipe
  • Derguene Mbaye
  • Amelia Taylor
  • Fatoumata Ouoba Kabore
  • Chris Chinenye Emezue
  • Aremu Anuoluwapo
  • Perez Ogayo
  • Catherine Gitau
  • Edwin Munkoh-Buabeng
  • Victoire Memdjokam Koagne
  • Allahsera Auguste Tapo
  • Tebogo Macucwa
  • Vukosi Marivate
  • Elvis Mboning
  • Tajuddeen Gwadabe
  • Tosin P. Adewumi
  • Orevaoghene Ahia
  • Joyce Nakatumba-Nabende
  • Neo L. Mokono
  • Mofetoluwa Adeyemi
  • Gilles Hacheme
  • Idris Abdulmumin
  • Odunayo Ogundepo
  • Oreen Yousuf
  • Tatiana Moteu Ngoli
  • Dietrich Klakow
<mark>Journal publication date</mark>15/11/2022
Number of pages22
Publication StatusPublished
<mark>Original language</mark>English


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 of annotated datasets, as well as a lack of understanding of the settings where current methods are effective. In this paper, we make progress towards solutions for these challenges, focusing on the task of named entity recognition (NER). We create the largest human-annotated NER dataset for 20 African languages, and we study the behavior of state-of-the-art cross-lingual transfer methods in an Africa-centric setting, demonstrating that the choice of source language significantly affects performance. We show that choosing the best transfer language improves zero-shot F1 scores by an average of 14 points across 20 languages compared to using English. Our results highlight the need for benchmark datasets and models that cover typologically-diverse African languages.