David Ifeoluwa Adelani, Graham Neubig, Sebastian Ruder, Shruti Rijhwani, Michael Beukman, Chester Palen-Michel, Constantine Lignos, Jesujoba Oluwadara Alabi, Shamsuddeen Hassan Muhammad, Peter Nabende, Cheikh M. Bamba Dione, Andiswa Bukula, Rooweither Mabuya, Bonaventure F. P. Dossou, Blessing K. Sibanda, Happy Buzaaba, Jonathan Mukiibi, Godson Kalipe, Derguene Mbaye, Amelia Taylor, F. Kabore, Chris C. Emezue, Anuoluwapo Aremu, Perez Ogayo, C. Gitau, Edwin Munkoh-Buabeng, V. M. Koagne, A. Tapo, Tebogo Macucwa, Vukosi Marivate, Elvis Mboning, T. Gwadabe, Tosin P. Adewumi, Orevaoghene Ahia, J. Nakatumba‐Nabende, Neo L. Mokono, Ignatius M Ezeani, C. Chukwuneke, Mofetoluwa Adeyemi, Gilles Hacheme, Idris Abdulmumin, Odunayo Ogundepo, Oreen Yousuf, Tatiana Moteu Ngoli, D. Klakow

Conference on Empirical Methods in Natural Language Processing

Paper

Abstract

African languages are spoken by over a billion people, but they are under-represented in NLP research and development. Multiple challenges exist, including the limited availability of annotated training and evaluation datasets as well as the lack of understanding of which settings, languages, and recently proposed methods like cross-lingual transfer will be effective. In this paper, we aim to move towards solutions for these challenges, focusing on the task of named entity recognition (NER). We present the creation of the largest to-date human-annotated NER dataset for 20 African languages. We study the behaviour of state-of-the-art cross-lingual transfer methods in an Africa-centric setting, empirically demonstrating that the choice of source transfer language significantly affects performance. While much previous work defaults to using English as the source language, our results show that choosing the best transfer language improves zero-shot F1 scores by an average of 14% over 20 languages as compared to using English.