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

Adelani, David Ifeoluwa; Neubig, Graham; Ruder, Sebastian; Rijhwani, Shruti; Michael, Beukman,; Palen-Michel, Chester; Lignos, Constantine; Alabi, Jesujoba O.; Muhammad, Shamsuddeen Hassan; Nabende, Peter; Dione, Cheikh M. Bamba; Bukula, Andiswa; Mabuya, Rooweither; Dossou, Bonaventure F. P.; Buzaaba, Happy; Mukiibi, Jonathan; Kalipe, Godson; Mbaye, Derguene; Taylor, A. M. Robert; Kabore, Fatoumata; Emezue, Chris Chinenye; Aremu, Anuoluwapo; Perez, Ogayo,; Gitau, Catherine W.; Edwin, Munkoh-Buabeng,; Memdjokam, Koagne, victoire; Tapo, Allahsera Auguste; Macucwa, Tebogo; Marivate, Vukosi; Mboning, Elvis; Tajuddeen, Gwadabe,; Adewumi, Tosin; Ahia, Orevaoghene; Nakatumba‐Nabende, Joyce; Mokono, Neo Lerato; Ezeani, Ignatius; Chiamaka, Chukwuneke,; Adeyemi, Mofetoluwa; Hacheme, Gilles Q.; Abdulmumin, Idris; Ogundepo, Odunayo; Yousuf, Oreen; Ngoli, Tatiana Moteu; Klakow, Dietrich

doi:10.48550/arxiv.2210.12391

Cited by 1 publication

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“…The teams of volunteers are usually natives, language experts from universities, researchers, and other community volunteers who contribute wellcurated translations. Masakhane has also worked on other NER datasets such as MasakhaNER [5] and MasakhaNER 2.0 [29]. Sunbird AI [30] has also recently started efforts to create open datasets for African languages.…”

Section: Text Datasetsmentioning

confidence: 99%

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

Nakatumba‐Nabende,

Babirye,

Nabende

et al. 2024

Applied AI Letters

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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.

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Section: Text Datasetsmentioning

confidence: 99%