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1. Building Text and Speech Benchmark Datasets and Models for Low‐Resourced East African Languages: Experiences and Lessons

Author

Joyce Nakatumba‐Nabende, Claire Babirye, Peter Nabende, Jeremy Francis Tusubira, Jonathan Mukiibi, Eric Peter Wairagala, Chodrine Mutebi, Tobius Saul Bateesa, Alvin Nahabwe, Hewitt Tusiime, and Andrew Katumba

Subjects
automatic speech recognition, low‐resourced language, machine translation, speech dataset, text dataset, topic modeling, Electronic computers. Computer science, QA75.5-76.95
Abstract

ABSTRACT Africa has over 2000 languages; however, those languages are not well represented in the existing natural language processing ecosystem. African languages lack essential digital resources to effectively engage in advancing language technologies. There is a need to generate high‐quality natural language processing resources for low‐resourced African languages. Obtaining high‐quality speech and text data is expensive and tedious because it can involve manual sourcing and verification of data sources. This paper discusses the process taken to curate and annotate text and speech datasets for five East African languages: Luganda, Runyankore‐Rukiga, Acholi, Lumasaba, and Swahili. We also present results obtained from baseline models for machine translation, topic modeling and classification, sentiment classification, and automatic speech recognition tasks. Finally, we discuss the experiences, challenges, and lessons learned in creating the text and speech datasets.

Published
2024
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2. MasakhaNER: Named Entity Recognition for African Languages

Author

David Ifeoluwa Adelani, Jade Abbott, Graham Neubig, Daniel D’souza, Julia Kreutzer, Constantine Lignos, Chester Palen-Michel, Happy Buzaaba, Shruti Rijhwani, Sebastian Ruder, Stephen Mayhew, Israel Abebe Azime, Shamsuddeen H. Muhammad, Chris Chinenye Emezue, Joyce Nakatumba-Nabende, Perez Ogayo, Aremu Anuoluwapo, Catherine Gitau, Derguene Mbaye, Jesujoba Alabi, Seid Muhie Yimam, Tajuddeen Rabiu Gwadabe, Ignatius Ezeani, Rubungo Andre Niyongabo, Jonathan Mukiibi, Verrah Otiende, Iroro Orife, Davis David, Samba Ngom, Tosin Adewumi, Paul Rayson, Mofetoluwa Adeyemi, Gerald Muriuki, Emmanuel Anebi, Chiamaka Chukwuneke, Nkiruka Odu, Eric Peter Wairagala, Samuel Oyerinde, Clemencia Siro, Tobius Saul Bateesa, Temilola Oloyede, Yvonne Wambui, Victor Akinode, Deborah Nabagereka, Maurice Katusiime, Ayodele Awokoya, Mouhamadane MBOUP, Dibora Gebreyohannes, Henok Tilaye, Kelechi Nwaike, Degaga Wolde, Abdoulaye Faye, Blessing Sibanda, Orevaoghene Ahia, Bonaventure F. P. Dossou, Kelechi Ogueji, Thierno Ibrahima DIOP, Abdoulaye Diallo, Adewale Akinfaderin, Tendai Marengereke, and Salomey Osei

Subjects
Computational linguistics. Natural language processing, P98-98.5
Abstract

AbstractWe take a step towards addressing the under- representation of the African continent in NLP research by bringing together different stakeholders to create the first large, publicly available, 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

Published
2021
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3. MasakhaNER: Named entity recognition for African languages

Author

Julia Kreutzer, Ayodele Awokoya, Ignatius Ezeani, Rubungo Andre Niyongabo, Happy Buzaaba, Adewale Akinfaderin, Samuel Oyerinde, Stephen Mayhew, Emmanuel Anebi, Mofetoluwa Adeyemi, Kelechi Ogueji, Abdoulaye Diallo, Seid Muhie Yimam, Jade Abbott, Joyce Nakatumba-Nabende, Victor Akinode, Blessing Sibanda, Catherine Gitau, Chester Palen-Michel, Shamsuddeen Hassan Muhammad, Degaga Wolde, Graham Neubig, Tendai Marengereke, Paul Rayson, Derguene Mbaye, Eric Peter Wairagala, Daniel D'souza, Tosin P. Adewumi, Jonathan Mukiibi, Chris Chinenye Emezue, David Ifeoluwa Adelani, Shruti Rijhwani, Iroro Orife, Verrah Otiende, Maurice Katusiime, Yvonne Wambui, Dibora Gebreyohannes, Kelechi Nwaike, Salomey Osei, Chiamaka Chukwuneke, Henok Tilaye, Deborah Nabagereka, Thierno Ibrahima Diop, Orevaoghene Ahia, Jesujoba O. Alabi, Sebastian Ruder, Davis David, Mouhamadane Mboup, Samba Ngom, Tajuddeen R. Gwadabe, Bonaventure F. P. Dossou, Temilola Oloyede, Perez Ogayo, Clemencia Siro, Gerald Muriuki, Aremu Anuoluwapo, Nkiruka Odu, Tobius Saul Bateesa, Abdoulaye Faye, Israel Abebe Azime, Constantine Lignos, Saarland University [Saarbrücken], Masakhane NLP, Retro Rabbit, Carnegie Mellon University [Pittsburgh] (CMU), ProQuest, Google Research, Brandeis University, Université de Tsukuba = University of Tsukuba, DeepMind, DeepMind Technologies, Duolingo, African Institute for Mathematical Sciences (AIMS), University of Porto, Bayero University Kano (BUK), Technische Universität Munchen - Université Technique de Munich [Munich, Allemagne] (TUM), Makerere University [Kampala, Ouganda] (MAK), African Leadership University, University of Lagos, Max Planck Institute for Informatics [Saarbrücken], Universität Hamburg (UHH), University of Chinese Academy of Sciences [Beijing] (UCAS), Lancaster University, University of Electronic Science and Technology of China (UESTC), United States International University - Africa, Niger-Volta Language Technologies Institute, Luleå University of Technology (LUT), African University of Science and Technology (AUST), University of Ibadan, Namibia University of Science and Technology (NUST), InstaDeep, Jacobs University [Bremen], University of Waterloo [Waterloo], European Project: 825081,H2020,COMPRISE(2018), Technical University of Munich (TUM), DeepMind [London], Universidade do Porto = University of Porto, and University of Electronic Science and Technology of China [Chengdu] (UESTC)

Subjects
FOS: Computer and information sciences, Linguistics and Language, Computer Science - Computation and Language, Computer science, business.industry, Computer Science - Artificial Intelligence, Communication, Languages of Africa, computer.software_genre, Code (semiotics), [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], Computer Science Applications, Human-Computer Interaction, Artificial Intelligence (cs.AI), Named-entity recognition, Artificial Intelligence, Artificial intelligence, Transfer of learning, business, computer, Computation and Language (cs.CL), Natural language processing
Abstract

We take a step towards addressing the under-representation of the African continent in NLP research by creating the first large publicly available high-quality dataset for named entity recognition (NER) in ten African languages, bringing together a variety of stakeholders. We detail characteristics of the languages to help researchers understand the challenges that these languages pose for NER. We analyze our datasets and conduct an extensive empirical evaluation of state-of-the-art methods across both supervised and transfer learning settings. We release the data, code, and models in order to inspire future research on African NLP., Comment: Accepted to TACL 2021, pre-MIT Press publication version

Published
2021
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