# AFRIDOC-MT: Document-level MT Corpus for African Languages

arXiv: https://arxiv.org/abs/2501.06374 · HTML: https://mlsanity.com/paper/2501.06374/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 42 of its 86 references that are in the map.
- First posted on arXiv 10 Jan 2025.

## Builds on (most cited first, 30 of 42)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [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)
- [Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model](https://mlsanity.com/paper/2402.07827/) (2024)
- [WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia](https://mlsanity.com/paper/1907.05791/) (2019)
- [Lessons from the Trenches on Reproducible Evaluation of Language Models](https://mlsanity.com/paper/2405.14782/) (2024)
- [CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB](https://mlsanity.com/paper/1911.04944/) (2019)
- [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)
- [A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translation](https://mlsanity.com/paper/2205.02022/) (2022)
- [Context-Aware Neural Machine Translation Learns Anaphora Resolution](https://mlsanity.com/paper/1805.10163/) (2018)
- [Neural Machine Translation with Extended Context](https://mlsanity.com/paper/1708.05943/) (2017)
- [Document-Level Machine Translation with Large Language Models](https://mlsanity.com/paper/2304.02210/) (2023)
- [Evaluating Discourse Phenomena in Neural Machine Translation](https://mlsanity.com/paper/1711.00513/) (2017)
- [Document-Level Neural Machine Translation with Hierarchical Attention Networks](https://mlsanity.com/paper/1809.01576/) (2018)
- [TICO-19: the Translation Initiative for Covid-19](https://mlsanity.com/paper/2007.01788/) (2020)
- [Error Span Annotation: A Balanced Approach for Human Evaluation of Machine Translation](https://mlsanity.com/paper/2406.11580/) (2024)
- [Adapting Large Language Models for Document-Level Machine Translation](https://mlsanity.com/paper/2401.06468/) (2024)
- [BlonDe: An Automatic Evaluation Metric for Document-level Machine Translation](https://mlsanity.com/paper/2103.11878/) (2021)
- [An Open Dataset and Model for Language Identification](https://mlsanity.com/paper/2305.13820/) (2023)
- [On The Evaluation of Machine Translation Systems Trained With Back-Translation](https://mlsanity.com/paper/1908.05204/) (2019)
- [The Effect of Domain and Diacritics in Yor`ub'a-English Neural Machine Translation](https://mlsanity.com/paper/2103.08647/) (2021)
- [Attention Strategies for Multi-Source Sequence-to-Sequence Learning](https://mlsanity.com/paper/1704.06567/) (2017)
- [AfriMTE and AfriCOMET: Enhancing COMET to Embrace Under-resourced African Languages](https://mlsanity.com/paper/2311.09828/) (2023)

## Built on by (most cited first, 11 of 11)

- [DocHPLT: A Massively Multilingual Document-Level Translation Dataset](https://mlsanity.com/paper/2508.13079/) (2025)
- [What Does LLM Refinement Actually Improve? A Systematic Study on Document-Level Literary Translation](https://mlsanity.com/paper/2605.13368/) (2026)
- [OpenWHO: A Document-Level Parallel Corpus for Health Translation in Low-Resource Languages](https://mlsanity.com/paper/2508.16048/) (2025)
- [Automatic Correction of Writing Anomalies in Hausa Texts](https://mlsanity.com/paper/2506.03820/) (2025)
- [AmharicStoryQA: A Multicultural Story Question Answering Benchmark in Amharic](https://mlsanity.com/paper/2602.02774/) (2026)
- [AfrIFact: Cultural Information Retrieval, Evidence Extraction and Fact Checking for African Languages](https://mlsanity.com/paper/2604.00706/) (2026)
- [Mining Large Language Models for Low-Resource Language Data: Comparing Elicitation Strategies for Hausa and Fongbe](https://mlsanity.com/paper/2604.12477/) (2026)
- [AfriScience-MT: Towards Decolonizing Science in Africa through Text Translation](https://mlsanity.com/paper/2605.29741/) (2026)
- [Evaluating Large Language Models for Hausa and Fongbe Machine Translation: Benchmarks, Failures, and Metric Reliability](https://mlsanity.com/paper/2606.22269/) (2026)
- [TranslatePsy-AfriSLM: High-Quality Data Scaling For Low-Resource Machine Translation](https://mlsanity.com/paper/2608.18655/) (2026)
- [Doc2FRC: Length-Consistent Document-Level Machine Translation via Fixed-Range Chunking](https://mlsanity.com/paper/2609.12674/) (2026)

## Abstract (arXiv)

This paper introduces AFRIDOC-MT, a document-level multi-parallel translation dataset covering English and five African languages: Amharic, Hausa, Swahili, Yor`ub'a, and Zulu. The dataset comprises 334 health and 271 information technology news documents, all human-translated from English to these languages. We conduct document-level translation benchmark experiments by evaluating neural machine translation (NMT) models and large language models (LLMs) for translations between English and these languages, at both the sentence and pseudo-document levels. These outputs are realigned to form complete documents for evaluation. Our results indicate that NLLB-200 achieved the best average performance among the standard NMT models, while GPT-4o outperformed general-purpose LLMs. Fine-tuning selected models led to substantial performance gains, but models trained on sentences struggled to generalize effectively to longer documents. Furthermore, our analysis reveals that some LLMs exhibit issues such as under-generation, repetition of words or phrases, and off-target translations, especially for African languages.