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

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 4488 - 4508 December 7-11, 2022 ©2022 Association for Computational Linguistics MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition David Ifeoluwa Adelani1,2,∗, Graham Neubig3, Sebastian Ruder4, Shruti Rijhwani3, Michael Beukman5∗, Chester Palen-Michel6∗, Constantine Lignos6∗, Jesujoba O. Alabi1∗, Shamsuddeen H. Muhammad7∗, Peter Nabende8∗, Cheikh M. Bamba Dione9∗, Andiswa Bukula10, Rooweither Mabuya10, Bonaventure F. P. Dossou11∗, Blessing Sibanda∗, Happy Buzaaba12∗, Jonathan Mukiibi8∗, Godson Kalipe∗, Derguene Mbaye13∗, Amelia Taylor14∗, Fatoumata Kabore15∗, Chris Chinenye Emezue16∗, Anuoluwapo Aremu∗, Perez Ogayo3∗, Catherine Gitau∗, Edwin Munkoh-Buabeng17∗, Victoire M. Koagne∗, Allahsera Auguste Tapo18∗, Tebogo Macucwa19∗, Vukosi Marivate19∗, Elvis Mboning∗, Tajuddeen Gwadabe∗, Tosin Adewumi20∗, Orevaoghene Ahia21∗, Joyce Nakatumba-Nabende8∗, Neo L. Mokono19∗, Ignatius Ezeani22∗, Chiamaka Chukwuneke22∗, Mofetoluwa Adeyemi23∗, Gilles Q. Hacheme24∗, Idris Abdulmumin25∗, Odunayo Ogundepo23∗, Oreen Yousuf15∗, Tatiana Moteu Ngoli∗, Dietrich Klakow1 ∗Masakhane NLP, 1Saarland University, Germany, 2University College London, UK, 3Carnegie Mellon University, USA, 4Google Research, 5University of the Witwatersrand, South Africa, 6 Brandeis University, USA, 7LIAAD-INESC TEC, Portugal, 8Makerere University, Uganda 9University of Bergen, Norway, 10SADiLaR, South Africa, 11Mila Quebec AI Institute, Canada, 12RIKEN Center for AI Project, Japan, 13Baamtu, Senegal, 14Malawi University of Business and Applied Science, Malawi, 15Uppsala University, Sweden, 16TU Munich, Germany, 17TU Clausthal, Germany, 18Rochester Institute of Technology, USA, 19University of Pretoria, South Africa, 20Luleå University of Technology, Sweden, 21University of Washington, USA, 22Lancaster University, UK, 23University of Waterloo, Canada, 24Ai4innov, France, 25Ahmadu Bello University, Nigeria. Abstract African languages are spoken by over a bil- lion people, but are underrepresented in NLP research and development. The challenges im- peding progress include the limited availability of annotated datasets, as well as a lack of under- standing 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 lan- guage 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. 1 Introduction Many African languages are spoken by millions or tens of millions of speakers. However, these languages are poorly represented in NLP research, and the development of NLP systems for African languages is often limited by the lack of datasets for training and evaluation (Adelani et al., 2021b). Additionally, while there has been much re- cent work in using zero-shot cross-lingual trans- fer (Ponti et al., 2020; Pfeiffer et al., 2020; Ebrahimi et al., 2022) to improve performance on tasks for low-resource languages with multilin- gual pretrained language models (PLMs) (Devlin et al., 2019a; Conneau et al., 2020), the settings un- der which contemporary transfer learning methods work best are still unclear (Pruksachatkun et al., 2020; Lauscher et al., 2020; …

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