mlsanityData to 14 Oct 2026

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

arXiv 2511.06531 ↗First posted 9 Nov 2025cs.CL cs.AIcited by 6

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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.

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

23 of 47 references.

References · 23

  1. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
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  5. 2021The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translationcited by 560
  6. 2022FLEURS: Few-shot Learning Evaluation of Universal Representations of Speechcited by 469
  7. 2023SIB-200: A Simple, Inclusive, and Big Evaluation Dataset for Topic Classification in 200+ Languages and Dialectscited by 152
  8. 2022Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-Tuningcited by 149
  9. 2025FineWeb2: One Pipeline to Scale Them All -- Adapting Pre-Training Data Processing to Every Languagecited by 143
  10. 2023Glot500: Scaling Multilingual Corpora and Language Models to 500 Languagescited by 116
  11. 2022A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translationcited by 98
  12. 2022SERENGETI: Massively Multilingual Language Models for Africacited by 40
11 more
  1. 2021The Effect of Domain and Diacritics in Yor`ub'a-English Neural Machine Translationcited by 38
  2. 2021Many-to-English Machine Translation Tools, Data, and Pretrained Modelscited by 31
  3. 2023AfriMTE and AfriCOMET: Enhancing COMET to Embrace Under-resourced African Languagescited by 24
  4. 2024GlotCC: An Open Broad-Coverage CommonCrawl Corpus and Pipeline for Minority Languagescited by 16
  5. 2020Igbo-English Machine Translation: An Evaluation Benchmarkcited by 14
  6. 2023NollySenti: Leveraging Transfer Learning and Machine Translation for Nigerian Movie Sentiment Classificationcited by 14
  7. 2025SMOL: Professionally translated parallel data for 115 under-represented languagescited by 12
  8. 2025SSA-COMET: Do LLMs Outperform Learned Metrics in Evaluating MT for Under-Resourced African Languages?cited by 10
  9. 2023Bilex Rx: Lexical Data Augmentation for Massively Multilingual Machine Translationcited by 8
  10. 2024Mitigating Translationese in Low-resource Languages: The Storyboard Approachcited by 2
  11. 2025Natural language processing for African languagescited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 6.

6 papers.

  1. 2026Developing an English-Efik Corpus and Machine Translation System for Digitization Inclusioncited by 1
  2. 2026AfriSUD: A Dependency Treebank Collection for Evaluating Models on African Languagescited by 1
  3. 2026Towards Digital Preservation of Efik: TTS for a Low-Resource African Languagecited by 1
  4. 2026An Empirical Study of Many-Shot In-Context Learning for Machine Translation of Low-Resource Languagescited by 0
  5. 2026Dependency Parsing Across the Resource Spectrum: Evaluating Architectures on High and Low-Resource Languagescited by 0
  6. 2026Measuring the Cost of Variety Conflation in Multilingual MT Evaluation: Adding Mozambican Xichangana, Nyanja and Sena to FLORES+cited by 0