@inproceedings{alabi-etal-2025-afridoc,
title = "{AFRIDOC}-{MT}: Document-level {MT} Corpus for {A}frican Languages",
author = "Alabi, Jesujoba Oluwadara and
Azime, Israel Abebe and
Zhang, Miaoran and
Espa{\~n}a-Bonet, Cristina and
Bawden, Rachel and
Zhu, Dawei and
Adelani, David Ifeoluwa and
Odoje, Clement Oyeleke and
Akinade, Idris and
Maab, Iffat and
David, Davis and
Muhammad, Shamsuddeen Hassan and
Putini, Neo and
Ademuyiwa, David O. and
Caines, Andrew and
Klakow, Dietrich",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1413/",
pages = "27758--27794",
ISBN = "979-8-89176-332-6",
abstract = "This paper introduces AFRIDOC-MT, a document-level multi-parallel translation dataset covering English and five African languages: Amharic, Hausa, Swahili, Yor{\`u}b{\'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 the ability of neural machine translation (NMT) models and large language models (LLMs) to translate between English and these languages, at both the sentence and pseudo-document levels, the outputs being realigned to form complete documents for evaluation. Our results indicate that NLLB-200 achieves the best average performance among the standard NMT models, while GPT-4o outperforms general-purpose LLMs. Fine-tuning selected models leads to substantial performance gains, but models trained on sentences struggle to generalize effectively to longer documents. Furthermore, our analysis reveals that some LLMs exhibit issues such as under-generation, over-generation, repetition of words and phrases, and off-target translations, specifically for translation into African languages."
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<abstract>This paper introduces AFRIDOC-MT, a document-level multi-parallel translation dataset covering English and five African languages: Amharic, Hausa, Swahili, Yorùbá, 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 the ability of neural machine translation (NMT) models and large language models (LLMs) to translate between English and these languages, at both the sentence and pseudo-document levels, the outputs being realigned to form complete documents for evaluation. Our results indicate that NLLB-200 achieves the best average performance among the standard NMT models, while GPT-4o outperforms general-purpose LLMs. Fine-tuning selected models leads to substantial performance gains, but models trained on sentences struggle to generalize effectively to longer documents. Furthermore, our analysis reveals that some LLMs exhibit issues such as under-generation, over-generation, repetition of words and phrases, and off-target translations, specifically for translation into African languages.</abstract>
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%0 Conference Proceedings
%T AFRIDOC-MT: Document-level MT Corpus for African Languages
%A Alabi, Jesujoba Oluwadara
%A Azime, Israel Abebe
%A Zhang, Miaoran
%A España-Bonet, Cristina
%A Bawden, Rachel
%A Zhu, Dawei
%A Adelani, David Ifeoluwa
%A Odoje, Clement Oyeleke
%A Akinade, Idris
%A Maab, Iffat
%A David, Davis
%A Muhammad, Shamsuddeen Hassan
%A Putini, Neo
%A Ademuyiwa, David O.
%A Caines, Andrew
%A Klakow, Dietrich
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F alabi-etal-2025-afridoc
%X This paper introduces AFRIDOC-MT, a document-level multi-parallel translation dataset covering English and five African languages: Amharic, Hausa, Swahili, Yorùbá, 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 the ability of neural machine translation (NMT) models and large language models (LLMs) to translate between English and these languages, at both the sentence and pseudo-document levels, the outputs being realigned to form complete documents for evaluation. Our results indicate that NLLB-200 achieves the best average performance among the standard NMT models, while GPT-4o outperforms general-purpose LLMs. Fine-tuning selected models leads to substantial performance gains, but models trained on sentences struggle to generalize effectively to longer documents. Furthermore, our analysis reveals that some LLMs exhibit issues such as under-generation, over-generation, repetition of words and phrases, and off-target translations, specifically for translation into African languages.
%U https://aclanthology.org/2025.emnlp-main.1413/
%P 27758-27794
Markdown (Informal)
[AFRIDOC-MT: Document-level MT Corpus for African Languages](https://aclanthology.org/2025.emnlp-main.1413/) (Alabi et al., EMNLP 2025)
ACL
- Jesujoba Oluwadara Alabi, Israel Abebe Azime, Miaoran Zhang, Cristina España-Bonet, Rachel Bawden, Dawei Zhu, David Ifeoluwa Adelani, Clement Oyeleke Odoje, Idris Akinade, Iffat Maab, Davis David, Shamsuddeen Hassan Muhammad, Neo Putini, David O. Ademuyiwa, Andrew Caines, and Dietrich Klakow. 2025. AFRIDOC-MT: Document-level MT Corpus for African Languages. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 27758–27794, Suzhou, China. Association for Computational Linguistics.