Ife Adebara, Hawau Olamide Toyin, Nahom Tesfu Ghebremichael, AbdelRahim A. Elmadany, Muhammad Abdul-Mageed. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Low-resource African languages pose unique challenges for natural language processing (NLP) tasks, including natural language generation (NLG).In this paper, we develop Cheetah, a massively multilingual NLG language model for African languages.Cheetah supports 517 African languages and language varieties, allowing us to address the scarcity of NLG resources and provide a solution to foster linguistic diversity.We demonstrate the effectiveness of Cheetah through comprehensive evaluations across six generation downstream tasks.In five of the six tasks, Cheetah significantly outperforms other models, showcasing its remarkable performance for generating coherent and contextually appropriate text in a wide range of African languages.We additionally conduct a detailed human evaluation to delve deeper into the linguistic capabilities of Cheetah.The findings of this study contribute to advancing NLP research in low-resource settings, enabling greater accessibility and inclusion for African languages in a rapidly expanding digital landscape.The GitHub repository for the Cheetah project is available at https://github.com

Wei-Rui Chen, Ife Adebara, Muhammad Abdul-Mageed. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

Aligning with ACL 2022 special Theme on "Language Diversity: from Low Resource to Endangered Languages", we discuss the major linguistic and sociopolitical challenges facing development of NLP technologies for African languages.Situating African languages in a typological framework, we discuss how the particulars of these languages can be harnessed.To facilitate future research, we also highlight current efforts, communities, venues, datasets, and tools.Our main objective is to motivate and advocate for an Afrocentric approach to technology development.With this in mind, we recommend what technologies to build and how to build, evaluate, and deploy them based on the needs of local African communities.

Translating between languages where certain features are marked morphologically in one but absent or marked contextually in the other is an important test case for machine translation. When translating into English which marks (in)definiteness morphologically, from Yorùbá which uses bare nouns but marks these features contextually, ambiguities arise. In this work, we perform fine-grained analysis on how an SMT system compares with two NMT systems (BiLSTM and Transformer) when translating bare nouns in Yorùbá into English. We investigate how the systems what extent they identify BNs, correctly translate them, and compare with human translation patterns. We also analyze the type of errors each model makes and provide a linguistic description of these errors. We glean insights for evaluating model performance in low-resource settings. In translating bare nouns, our results show the transformer model outperforms the SMT and BiLSTM models for 4 categories, the BiLSTM outperforms the SMT model for 3 categories while the SMT outperforms the NMT models for 1 category.

Language identification (LID) is a crucial precursor for NLP, especially for mining web data.Problematically, most of the world's 7000+ languages today are not covered by LID technologies.We address this pressing issue for Africa by introducing AfroLID, a neural LID toolkit for 517 African languages and varieties.AfroLID exploits a multi-domain web dataset manually curated from across 14 language families utilizing five orthographic systems.When evaluated on our blind Test set, AfroLID achieves 95.89 F 1 -score.We also compare AfroLID to five existing LID tools that each cover a small number of African languages, finding it to outperform them on most languages.We further show the utility of AfroLID in the wild by testing it on the acutely under-served Twitter domain.Finally, we offer a number of controlled case studies and perform a linguistically-motivated error analysis that allow us to both showcase AfroLID's powerful capabilities and limitations.1