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

arXiv: https://arxiv.org/abs/2511.06531 · HTML: https://mlsanity.com/paper/2511.06531/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 47 references that are in the map.
- First posted on arXiv 9 Nov 2025.

## Builds on (most cited first, 23 of 23)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [COMET: A Neural Framework for MT Evaluation](https://mlsanity.com/paper/2009.09025/) (2020)
- [Beyond English-Centric Multilingual Machine Translation](https://mlsanity.com/paper/2010.11125/) (2020)
- [The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation](https://mlsanity.com/paper/2106.03193/) (2021)
- [FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech](https://mlsanity.com/paper/2205.12446/) (2022)
- [SIB-200: A Simple, Inclusive, and Big Evaluation Dataset for Topic Classification in 200+ Languages and Dialects](https://mlsanity.com/paper/2309.07445/) (2023)
- [Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-Tuning](https://mlsanity.com/paper/2204.06487/) (2022)
- [FineWeb2: One Pipeline to Scale Them All -- Adapting Pre-Training Data Processing to Every Language](https://mlsanity.com/paper/2506.20920/) (2025)
- [Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages](https://mlsanity.com/paper/2305.12182/) (2023)
- [A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translation](https://mlsanity.com/paper/2205.02022/) (2022)
- [SERENGETI: Massively Multilingual Language Models for Africa](https://mlsanity.com/paper/2212.10785/) (2022)
- [The Effect of Domain and Diacritics in Yor`ub'a-English Neural Machine Translation](https://mlsanity.com/paper/2103.08647/) (2021)
- [Many-to-English Machine Translation Tools, Data, and Pretrained Models](https://mlsanity.com/paper/2104.00290/) (2021)
- [AfriMTE and AfriCOMET: Enhancing COMET to Embrace Under-resourced African Languages](https://mlsanity.com/paper/2311.09828/) (2023)
- [GlotCC: An Open Broad-Coverage CommonCrawl Corpus and Pipeline for Minority Languages](https://mlsanity.com/paper/2410.23825/) (2024)
- [Igbo-English Machine Translation: An Evaluation Benchmark](https://mlsanity.com/paper/2004.00648/) (2020)
- [NollySenti: Leveraging Transfer Learning and Machine Translation for Nigerian Movie Sentiment Classification](https://mlsanity.com/paper/2305.10971/) (2023)
- [SMOL: Professionally translated parallel data for 115 under-represented languages](https://mlsanity.com/paper/2502.12301/) (2025)
- [SSA-COMET: Do LLMs Outperform Learned Metrics in Evaluating MT for Under-Resourced African Languages?](https://mlsanity.com/paper/2506.04557/) (2025)
- [Bilex Rx: Lexical Data Augmentation for Massively Multilingual Machine Translation](https://mlsanity.com/paper/2303.15265/) (2023)
- [Mitigating Translationese in Low-resource Languages: The Storyboard Approach](https://mlsanity.com/paper/2407.10152/) (2024)
- [Natural language processing for African languages](https://mlsanity.com/paper/2507.00297/) (2025)

## Built on by (most cited first, 6 of 6)

- [Developing an English-Efik Corpus and Machine Translation System for Digitization Inclusion](https://mlsanity.com/paper/2603.14873/) (2026)
- [AfriSUD: A Dependency Treebank Collection for Evaluating Models on African Languages](https://mlsanity.com/paper/2606.12708/) (2026)
- [Towards Digital Preservation of Efik: TTS for a Low-Resource African Language](https://mlsanity.com/paper/2607.04515/) (2026)
- [An Empirical Study of Many-Shot In-Context Learning for Machine Translation of Low-Resource Languages](https://mlsanity.com/paper/2604.02596/) (2026)
- [Dependency Parsing Across the Resource Spectrum: Evaluating Architectures on High and Low-Resource Languages](https://mlsanity.com/paper/2605.02608/) (2026)
- [Measuring the Cost of Variety Conflation in Multilingual MT Evaluation: Adding Mozambican Xichangana, Nyanja and Sena to FLORES+](https://mlsanity.com/paper/2609.13847/) (2026)

## Abstract (arXiv)

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.