MasakhaNER: Named Entity Recognition for African Languages

MasakhaNER: Named Entity Recognition for African Languages David Ifeoluwa Adelani1∗, Jade Abbott2∗, Graham Neubig3, Daniel D’souza4∗, Julia Kreutzer5∗, Constantine Lignos6∗, Chester Palen-Michel6∗, Happy Buzaaba7∗, Shruti Rijhwani3, Sebastian Ruder8, Stephen Mayhew9, Israel Abebe Azime10∗, Shamsuddeen H. Muhammad11,12∗, Chris Chinenye Emezue13∗, Joyce Nakatumba-Nabende14∗, Perez Ogayo15∗, Aremu Anuoluwapo16∗, Catherine Gitau∗, Derguene Mbaye∗,Jesujoba Alabi17∗,Seid Muhie Yimam18,Tajuddeen Rabiu Gwadabe19∗, Ignatius Ezeani20∗,Rubungo Andre Niyongabo21∗,Jonathan Mukiibi14,Verrah Otiende22∗, Iroro Orife23∗, Davis David∗, Samba Ngom∗, Tosin Adewumi24∗, Paul Rayson20, Mofetoluwa Adeyemi∗, Gerald Muriuki14, Emmanuel Anebi∗, Chiamaka Chukwuneke20, Nkiruka Odu25, Eric Peter Wairagala14, Samuel Oyerinde∗, Clemencia Siro∗, Tobius Saul Bateesa14, Temilola Oloyede∗, Yvonne Wambui∗, Victor Akinode∗, Deborah Nabagereka14, Maurice Katusiime14, Ayodele Awokoya26∗, Mouhamadane MBOUP∗, Dibora Gebreyohannes∗, Henok Tilaye∗, Kelechi Nwaike∗, Degaga Wolde∗, Abdoulaye Faye∗, Blessing Sibanda27∗, Orevaoghene Ahia28∗, Bonaventure F. P. Dossou29∗, Kelechi Ogueji30∗, Thierno Ibrahima DIOP∗, Abdoulaye Diallo∗, Adewale Akinfaderin∗, Tendai Marengereke∗, and Salomey Osei10∗ ∗Masakhane NLP, 1Spoken Language Systems Group (LSV), Saarland University, Germany, 2Retro Rabbit, South Africa, 3Language Technologies Institute, Carnegie Mellon University, United States, 4ProQuest, United States, 5Google Research, Canada, 6Brandeis University, United States, 8DeepMind, United Kingdom, 9Duolingo, United States, 7Graduate School of Systems and Information Engineering, University of Tsukuba, Japan, 10African Institute for Mathematical Sciences (AIMS-AMMI), Ethiopia, 11University of Porto, Nigeria, 12Bayero University, Kano, Nigeria, 13Technical University of Munich, Germany 14 Makerere University, Kampala, Uganda,15African Leadership University, Rwanda 16University of Lagos, Nigeria, 17Max Planck Institute for Informatics, Germany, 18LT Group, Universit¨at Hamburg, Germany, 19University of Chinese Academy of Science, China 20Lancaster University, United Kingdom, 21University of Electronic Science and Technology of China, China, 22United States International University - Africa (USIU-A), Kenya, 23Niger-Volta LTI 24Lule˚o University of Technology, Sweden 25African University of Science and Technology, Abuja, Nigeria 26University of Ibadan, Nigeria, 27Namibia University of Science and Technology, Namibia 28Instadeep, Nigeria 29Jacobs University Bremen, Germany, 30 University of Waterloo, Canada Abstract We take a step towards addressing the under- representation of the African continent in NLP research by bringing together different stake- holders to create the first large, publicly avail- able, high-quality dataset for named entity recognition (NER) in ten African languages. We detail the characteristics of these languages to help researchers and practitioners better understand the challenges they pose for NER tasks. We analyze our datasets and conduct an extensive empirical evaluation of state- of-the-art methods across both supervised and transfer learning settings. Finally, we release the data, code, and models to inspire future research on African NLP.1 1https://git.io/masakhane-ner. 1 Introduction Africa has over 2,000 spoken languages (Eberhard et al., 2020); however, these languages are scarcely represented in existing natural language processing (NLP) datasets, research, and tools (Martinus and Abbott, 2019). ∀ (2020) investigate the reasons for these disparities by examining how NLP for low-resource languages is con- strained by several societal factors. One of these factors is the geographical and language diver- sity of NLP researchers. For example, of the 2695 affiliations of authors whose works were published at the five major NLP conferences in 2019, only five were from African institutions (Caines, 2019). Conversely, many NLP tasks such 1116 Transactions of the Association for …

Content truncated to 500 words