@inproceedings{tonja-etal-2026-afri,
title = "Afri-{MCQA}: Multimodal Cultural Question Answering for {A}frican Languages",
author = "Tonja, Atnafu Lambebo and
Anand, Srija and
Villa-Cueva, Emilio and
Azime, Israel Abebe and
Alabi, Jesujoba Oluwadara and
Mohamed, Muhidin A. and
Yadeta, Debela Desalegn and
Abadi, Negasi Haile and
Oppong, Abigail and
Obiefuna, Nnaemeka Casmir and
Abdulmumin, Idris and
Etori, Naome A and
Wairagala, Eric Peter and
Tshinu, Kanda Patrick and
Emmanuel, Imanigirimbabazi and
Malema, Gabofetswe and
Aji, Alham Fikri and
Adelani, David Ifeoluwa and
Solorio, Thamar",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1869/",
pages = "40249--40282",
ISBN = "979-8-89176-390-6",
abstract = "Africa is home to over one-third of the world{'}s languages, yet remains severely underrepresented in multimodal AI research. We introduce Afri-MCQA, the first Multilingual Cultural Question-Answering benchmark containing 7.5k Q A pairs across 15 African languages from 12 countries. The benchmark offers parallel text and speech modalities and was entirely created by native speakers. We find that models show poor performance across evaluated cultures, with near-zero accuracy on open-ended VQA when queried through native language or speech. To test linguistic competence, we include control experiments meant to assess this specific aspect separate from cultural knowledge, and we observe significant performance gaps between native languages and English for both text and speech. These findings underscore the pressing need for speech-first approaches, culturally grounded pretraining, and cross-lingual cultural transfer. We release Afri-MCQA to support more inclusive multimodal AI development."
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<abstract>Africa is home to over one-third of the world’s languages, yet remains severely underrepresented in multimodal AI research. We introduce Afri-MCQA, the first Multilingual Cultural Question-Answering benchmark containing 7.5k Q A pairs across 15 African languages from 12 countries. The benchmark offers parallel text and speech modalities and was entirely created by native speakers. We find that models show poor performance across evaluated cultures, with near-zero accuracy on open-ended VQA when queried through native language or speech. To test linguistic competence, we include control experiments meant to assess this specific aspect separate from cultural knowledge, and we observe significant performance gaps between native languages and English for both text and speech. These findings underscore the pressing need for speech-first approaches, culturally grounded pretraining, and cross-lingual cultural transfer. We release Afri-MCQA to support more inclusive multimodal AI development.</abstract>
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%0 Conference Proceedings
%T Afri-MCQA: Multimodal Cultural Question Answering for African Languages
%A Tonja, Atnafu Lambebo
%A Anand, Srija
%A Villa-Cueva, Emilio
%A Azime, Israel Abebe
%A Alabi, Jesujoba Oluwadara
%A Mohamed, Muhidin A.
%A Yadeta, Debela Desalegn
%A Abadi, Negasi Haile
%A Oppong, Abigail
%A Obiefuna, Nnaemeka Casmir
%A Abdulmumin, Idris
%A Etori, Naome A.
%A Wairagala, Eric Peter
%A Tshinu, Kanda Patrick
%A Emmanuel, Imanigirimbabazi
%A Malema, Gabofetswe
%A Aji, Alham Fikri
%A Adelani, David Ifeoluwa
%A Solorio, Thamar
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F tonja-etal-2026-afri
%X Africa is home to over one-third of the world’s languages, yet remains severely underrepresented in multimodal AI research. We introduce Afri-MCQA, the first Multilingual Cultural Question-Answering benchmark containing 7.5k Q A pairs across 15 African languages from 12 countries. The benchmark offers parallel text and speech modalities and was entirely created by native speakers. We find that models show poor performance across evaluated cultures, with near-zero accuracy on open-ended VQA when queried through native language or speech. To test linguistic competence, we include control experiments meant to assess this specific aspect separate from cultural knowledge, and we observe significant performance gaps between native languages and English for both text and speech. These findings underscore the pressing need for speech-first approaches, culturally grounded pretraining, and cross-lingual cultural transfer. We release Afri-MCQA to support more inclusive multimodal AI development.
%U https://aclanthology.org/2026.acl-long.1869/
%P 40249-40282
Markdown (Informal)
[Afri-MCQA: Multimodal Cultural Question Answering for African Languages](https://aclanthology.org/2026.acl-long.1869/) (Tonja et al., ACL 2026)
ACL
- Atnafu Lambebo Tonja, Srija Anand, Emilio Villa-Cueva, Israel Abebe Azime, Jesujoba Oluwadara Alabi, Muhidin A. Mohamed, Debela Desalegn Yadeta, Negasi Haile Abadi, Abigail Oppong, Nnaemeka Casmir Obiefuna, Idris Abdulmumin, Naome A Etori, Eric Peter Wairagala, Kanda Patrick Tshinu, Imanigirimbabazi Emmanuel, Gabofetswe Malema, Alham Fikri Aji, David Ifeoluwa Adelani, and Thamar Solorio. 2026. Afri-MCQA: Multimodal Cultural Question Answering for African Languages. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 40249–40282, San Diego, California, United States. Association for Computational Linguistics.