Ibom NLP: A Step Toward Inclusive Natural Language Processing for Nigeria's Minority Languages

• URL: http://arxiv.org/abs/2511.06531v1
• Date: Sun, 09 Nov 2025 20:33:39 GMT
• Title: Ibom NLP: A Step Toward Inclusive Natural Language Processing for Nigeria's Minority Languages
• Authors: Oluwadara Kalejaiye, Luel Hagos Beyene, David Ifeoluwa Adelani, Mmekut-Mfon Gabriel Edet, Aniefon Daniel Akpan, Eno-Abasi Urua, Anietie Andy,
• Abstract summary: Nigeria is the most populous country in Africa with a population of more than 200 million people.<n>More than 500 languages are spoken in Nigeria and it is one of the most linguistically diverse countries in the world.<n>Despite this, natural language processing (NLP) research has mostly focused on the following four languages: Hausa, Igbo, Nigerian-Pidgin, and Yoruba.
• Score: 5.5078606217036965
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Nigeria is the most populous country in Africa with a population of more than 200 million people. More than 500 languages are spoken in Nigeria and it is one of the most linguistically diverse countries in the world. Despite this, natural language processing (NLP) research has mostly focused on the following four languages: Hausa, Igbo, Nigerian-Pidgin, and Yoruba (i.e <1% of the languages spoken in Nigeria). This is in part due to the unavailability of textual data in these languages to train and apply NLP algorithms. In this work, we introduce ibom -- a dataset for machine translation and topic classification in four Coastal Nigerian languages from the Akwa Ibom State region: Anaang, Efik, Ibibio, and Oro. These languages are not represented in Google Translate or in major benchmarks such as Flores-200 or SIB-200. We focus on extending Flores-200 benchmark to these languages, and further align the translated texts with topic labels based on SIB-200 classification dataset. Our evaluation shows that current LLMs perform poorly on machine translation for these languages in both zero-and-few shot settings. However, we find the few-shot samples to steadily improve topic classification with more shots.

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