@inproceedings{villa-cueva-etal-2025-cammt,
title = "{C}a{MMT}: Benchmarking Culturally Aware Multimodal Machine Translation",
author = "Villa-Cueva, Emilio and
Bolatzhanova, Sholpan and
Turmakhan, Diana and
Elzeky, Kareem and
Ademtew, Henok Biadglign and
Aji, Alham Fikri and
Araujo, Vladimir and
Azime, Israel Abebe and
Baek, Jinheon and
Belcavello, Frederico and
Cristobal, Fermin and
Cruz, Jan Christian Blaise and
Dabre, Mary and
Dabre, Raj and
Ehsan, Toqeer and
Etori, Naome A and
Farooqui, Fauzan and
Geng, Jiahui and
Ivetta, Guido and
Jayakumar, Thanmay and
Jeong, Soyeong and
Lim, Zheng Wei and
Mandal, Aishik and
Martinelli, Sof{\'i}a and
Mihaylov, Mihail Minkov and
Orel, Daniil and
Pramanick, Aniket and
Purkayastha, Sukannya and
Salazar, Israfel and
Song, Haiyue and
Timponi Torrent, Tiago and
Yadeta, Debela Desalegn and
Hamed, Injy and
Tonja, Atnafu Lambebo and
Solorio, Thamar",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.1220/",
pages = "22423--22441",
ISBN = "979-8-89176-335-7",
abstract = "Translating cultural content poses challenges for machine translation systems due to the differences in conceptualizations between cultures, where language alone may fail to convey sufficient context to capture region-specific meanings. In this work, we investigate whether images can act as cultural context in multimodal translation. We introduce CaMMT, a human-curated benchmark of over 5,800 triples of images along with parallel captions in English and regional languages. Using this dataset, we evaluate five Vision Language Models (VLMs) in text-only and text+image settings. Through automatic and human evaluations, we find that visual context generally improves translation quality, especially in handling Culturally-Specific Items (CSIs), disambiguation, and correct gender marking. By releasing CaMMT, our objective is to support broader efforts to build and evaluate multimodal translation systems that are better aligned with cultural nuance and regional variations."
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<abstract>Translating cultural content poses challenges for machine translation systems due to the differences in conceptualizations between cultures, where language alone may fail to convey sufficient context to capture region-specific meanings. In this work, we investigate whether images can act as cultural context in multimodal translation. We introduce CaMMT, a human-curated benchmark of over 5,800 triples of images along with parallel captions in English and regional languages. Using this dataset, we evaluate five Vision Language Models (VLMs) in text-only and text+image settings. Through automatic and human evaluations, we find that visual context generally improves translation quality, especially in handling Culturally-Specific Items (CSIs), disambiguation, and correct gender marking. By releasing CaMMT, our objective is to support broader efforts to build and evaluate multimodal translation systems that are better aligned with cultural nuance and regional variations.</abstract>
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%0 Conference Proceedings
%T CaMMT: Benchmarking Culturally Aware Multimodal Machine Translation
%A Villa-Cueva, Emilio
%A Bolatzhanova, Sholpan
%A Turmakhan, Diana
%A Elzeky, Kareem
%A Ademtew, Henok Biadglign
%A Aji, Alham Fikri
%A Araujo, Vladimir
%A Azime, Israel Abebe
%A Baek, Jinheon
%A Belcavello, Frederico
%A Cristobal, Fermin
%A Cruz, Jan Christian Blaise
%A Dabre, Mary
%A Dabre, Raj
%A Ehsan, Toqeer
%A Etori, Naome A.
%A Farooqui, Fauzan
%A Geng, Jiahui
%A Ivetta, Guido
%A Jayakumar, Thanmay
%A Jeong, Soyeong
%A Lim, Zheng Wei
%A Mandal, Aishik
%A Martinelli, Sofía
%A Mihaylov, Mihail Minkov
%A Orel, Daniil
%A Pramanick, Aniket
%A Purkayastha, Sukannya
%A Salazar, Israfel
%A Song, Haiyue
%A Timponi Torrent, Tiago
%A Yadeta, Debela Desalegn
%A Hamed, Injy
%A Tonja, Atnafu Lambebo
%A Solorio, Thamar
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F villa-cueva-etal-2025-cammt
%X Translating cultural content poses challenges for machine translation systems due to the differences in conceptualizations between cultures, where language alone may fail to convey sufficient context to capture region-specific meanings. In this work, we investigate whether images can act as cultural context in multimodal translation. We introduce CaMMT, a human-curated benchmark of over 5,800 triples of images along with parallel captions in English and regional languages. Using this dataset, we evaluate five Vision Language Models (VLMs) in text-only and text+image settings. Through automatic and human evaluations, we find that visual context generally improves translation quality, especially in handling Culturally-Specific Items (CSIs), disambiguation, and correct gender marking. By releasing CaMMT, our objective is to support broader efforts to build and evaluate multimodal translation systems that are better aligned with cultural nuance and regional variations.
%U https://aclanthology.org/2025.findings-emnlp.1220/
%P 22423-22441
Markdown (Informal)
[CaMMT: Benchmarking Culturally Aware Multimodal Machine Translation](https://aclanthology.org/2025.findings-emnlp.1220/) (Villa-Cueva et al., Findings 2025)
ACL
- Emilio Villa-Cueva, Sholpan Bolatzhanova, Diana Turmakhan, Kareem Elzeky, Henok Biadglign Ademtew, Alham Fikri Aji, Vladimir Araujo, Israel Abebe Azime, Jinheon Baek, Frederico Belcavello, Fermin Cristobal, Jan Christian Blaise Cruz, Mary Dabre, Raj Dabre, Toqeer Ehsan, Naome A Etori, Fauzan Farooqui, Jiahui Geng, Guido Ivetta, Thanmay Jayakumar, Soyeong Jeong, Zheng Wei Lim, Aishik Mandal, Sofía Martinelli, Mihail Minkov Mihaylov, Daniil Orel, Aniket Pramanick, Sukannya Purkayastha, Israfel Salazar, Haiyue Song, Tiago Timponi Torrent, Debela Desalegn Yadeta, Injy Hamed, Atnafu Lambebo Tonja, and Thamar Solorio. 2025. CaMMT: Benchmarking Culturally Aware Multimodal Machine Translation. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 22423–22441, Suzhou, China. Association for Computational Linguistics.