---
title: "A Few Thousand Translations Go a Long Way! — Adelani 2022"
description: "Large multilingual translation models are trained mostly on web-crawled text, so many African languages with millions of speakers are absent or barely…"
authors: ["David Ifeoluwa Adelani", "Jesujoba Oluwadara Alabi", "Angela Fan", "Julia Kreutzer", "Xiaoyu Shen", "Machel Reid", "Dana Ruiter", "Dietrich Klakow", "Peter Nabende", "Ernie Chang", "Tajuddeen Gwadabe", "Freshia Sackey", "Bonaventure F. P. Dossou", "Chris Emezue", "Colin Leong", "Michael Beukman", "Shamsuddeen H. Muhammad", "Guyo D. Jarso", "Oreen Yousuf", "Andre N. Niyongabo Rubungo", "Gilles Hacheme", "Eric Peter Wairagala", "Muhammad Umair Nasir", "Benjamin A. Ajibade", "Tunde Oluwaseyi Ajayi", "Yvonne Wambui Gitau", "Jade Abbott", "Mohamed Ahmed", "Millicent Ochieng", "Anuoluwapo Aremu", "Perez Ogayo", "Jonathan Mukiibi", "Fatoumata Ouoba Kabore", "Godson Koffi Kalipe", "Derguene Mbaye", "Allahsera Auguste Tapo", "Victoire M. Memdjokam Koagne", "Edwin Munkoh-Buabeng", "Valencia Wagner", "Idris Abdulmumin", "Ayodele Awokoya", "Happy Buzaaba", "Blessing Sibanda", "Andiswa Bukula", "Sam Manthalu"]
year: 2022
venue: "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies"
source: "https://aclanthology.org/2022.naacl-main.223/"
doi: "10.18653/v1/2022.naacl-main.223"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/adelani-2022-few-thousand-translations-go-long/"
---
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# A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translation

**Authors:** [David Ifeoluwa Adelani](https://md.research.elamur.ai/free/en/authors/david-ifeoluwa-adelani/), Jesujoba Oluwadara Alabi, [Angela Fan](https://md.research.elamur.ai/free/en/authors/angela-fan/), [Julia Kreutzer](https://md.research.elamur.ai/free/en/authors/julia-kreutzer/), [Xiaoyu Shen](https://md.research.elamur.ai/free/en/authors/xiaoyu-shen/), [Machel Reid](https://md.research.elamur.ai/free/en/authors/machel-reid/), Dana Ruiter, [Dietrich Klakow](https://md.research.elamur.ai/free/en/authors/dietrich-klakow/), [Peter Nabende](https://md.research.elamur.ai/free/en/authors/peter-nabende/), [Ernie Chang](https://md.research.elamur.ai/free/en/authors/ernie-chang/), [Tajuddeen Gwadabe](https://md.research.elamur.ai/free/en/authors/tajuddeen-gwadabe/), [Freshia Sackey](https://md.research.elamur.ai/free/en/authors/freshia-sackey/) et al. (45 authors) · **Year:** 2022 · **Venue:** Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2022.naacl-main.223/) · [PDF](https://aclanthology.org/2022.naacl-main.223.pdf) · [DOI 10.18653/v1/2022.naacl-main.223](https://doi.org/10.18653/v1/2022.naacl-main.223) · [OpenAlex](https://openalex.org/W4229014795)

**Citations:** 47 citations (openalex-snapshot · 2026-10-14) · [108](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
Large multilingual translation models are trained mostly on web-crawled text, so many African languages with millions of speakers are absent or barely represented. The available parallel data for these languages is largely religious (Bible, JW300). The authors ask two questions: how can such pre-trained models be extended to languages they never saw, and how can they move from religious text to news?

To study this, a large Masakhane team built MAFAND-MT, a news-domain benchmark for 16 African languages, each paired with English or French. They then adapted MT5, ByT5, mBART and M2M-100 in three ways: zero-shot use, continued pre-training on African monolingual text, and fine-tuning on parallel data from several domains.

The most effective approach was to fine-tune a large pre-trained model on a few thousand high-quality in-domain sentence pairs. This worked even for languages missing from pre-training. The finding shows that small, community-built datasets can give speakers working MT systems without relying on huge web crawls.

## Key points
- MAFAND-MT covers 16 languages from four families, 13 of them Niger-Congo. Six languages are paired with French, including Bambara, Fon and Wolof, and the rest with English. According to the authors, eight of these languages previously had no evaluation dataset at all.
- Models fine-tuned only on religious data lose most of their quality on news. For example, Ghomálá' (8k religious sentences) drops 95.5% in BLEU, while Naija has the smallest drop at 59.3%.
- About 2.5k news sentence pairs were enough for M2M-100 or ByT5 to beat bilingual Transformer baselines trained with extra religious data. This held for both seen (swa) and unseen (ibo, bam) languages.
- Fine-tuning M2M-100 on the news data also improved results on other domains. On FLORES (Wikipedia), en-ibo spBLEU rose from 2.8 to 19.9, and religious-domain scores improved as well.
- Data, models and code are released at github.com/masakhane-io/lafand-mt under an academic license.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

## Cited by

Cited by

108 papers of our library cite it (of the 140884 whose bibliographies we read); 8 of them have a page here, 100 not yet.

- [MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition](https://md.research.elamur.ai/free/en/papers/adelani-2022-masakhaner-2-0-africa-centric/)
- [SIB-200: A Simple, Inclusive, and Big Evaluation Dataset for Topic Classification in 200+ Languages and Dialects](https://md.research.elamur.ai/free/en/papers/adelani-2023-sib-200-simple-inclusive-big/)
- [AGE: Amharic, Ge'ez and English Parallel Dataset](https://md.research.elamur.ai/free/en/papers/ademtew-2024-age-amharic-ge-ez-english/)
- [Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-Tuning](https://md.research.elamur.ai/free/en/papers/alabi-2022-adapting-pre-trained-language-models/)
- [MzansiText and MzansiLM: An Open Corpus and Decoder-Only Language Model for South African Languages](https://md.research.elamur.ai/free/en/papers/lombard-2026-mzansitext-mzansilm-open-corpus-decoder/)
- [Evaluating Machine Translation Datasets for Low-Web Data Languages: A Gendered Lens](https://md.research.elamur.ai/free/en/papers/nigatu-2026-evaluating-machine-translation-datasets-low/)
- [Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model](https://md.research.elamur.ai/free/en/papers/ustun-2024-aya-model-instruction-finetuned-open/)
- [A Dataset of Wolof Ajami Manuscripts for HTR and OCR](https://md.research.elamur.ai/free/en/papers/yousuf-2026-dataset-wolof-ajami-manuscripts-htr/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{adelani2022few,
  title = {{A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translation}},
  author = {Adelani, David Ifeoluwa and Alabi, Jesujoba Oluwadara and Fan, Angela and Kreutzer, Julia and Shen, Xiaoyu and Reid, Machel and Ruiter, Dana and Klakow, Dietrich and Nabende, Peter and Chang, Ernie and Gwadabe, Tajuddeen and Sackey, Freshia and Dossou, Bonaventure F. P. and Emezue, Chris and Leong, Colin and Beukman, Michael and Muhammad, Shamsuddeen H. and Jarso, Guyo D. and Yousuf, Oreen and Rubungo, Andre N. Niyongabo and Hacheme, Gilles and Wairagala, Eric Peter and Nasir, Muhammad Umair and Ajibade, Benjamin A. and Ajayi, Tunde Oluwaseyi and Gitau, Yvonne Wambui and Abbott, Jade and Ahmed, Mohamed and Ochieng, Millicent and Aremu, Anuoluwapo and Ogayo, Perez and Mukiibi, Jonathan and Kabore, Fatoumata Ouoba and Kalipe, Godson Koffi and Mbaye, Derguene and Tapo, Allahsera Auguste and Koagne, Victoire M. Memdjokam and Munkoh-Buabeng, Edwin and Wagner, Valencia and Abdulmumin, Idris and Awokoya, Ayodele and Buzaaba, Happy and Sibanda, Blessing and Bukula, Andiswa and Manthalu, Sam},
  booktitle = {Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
  year = {2022},
  doi = {10.18653/v1/2022.naacl-main.223},
  url = {https://doi.org/10.18653/v1/2022.naacl-main.223}
}
```

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