@inproceedings{nacar-etal-2026-deam,
title = "{DEAM}: Diverse Embeddings for {A}rabic Multimodal Retrieval",
author = "Nacar, Omer and
Mohamed Emad Elden, Fatimah and
Al Hallay, Rahaf Saeed and
A.Azim, Mona and
Hamod, Aya and
Zaytoon, Mohamed and
Qandos, Nour and
Abdelaziz, Mohamed and
Elhosiny, Israa and
El-Ghawi, Yousra Alaa and
Sibaee, Serry and
Hamoud, Hadi and
Al-Zaid, Norah Naji and
Ragab, Muhammed and
Al Jallad, Khloud",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2329/",
pages = "52897--52913",
ISBN = "979-8-89176-459-0",
abstract = "Modern visual document retrieval (VDR) models remain strongly English-centric, leaving Arabic substantially under-served in this rapidly advancing field. To address this gap, we introduce DEAM, the first open Arabic visual document retrieval dataset explicitly designed for fine-tuning and evaluating multimodal retrievers on Arabic. DEAM is built through a human-in-the-loop construction pipeline with annotators community annotators, combining Arabic document rendering, query creation, validation, and retrieval-oriented hard-negative mining. The dataset comprises18,275 rendered Arabic documents spanning nine source domains and 82,727 contrastive retrieval triplets, each including five mined hard negatives and one easy negative for contrastive fine-tuning. Zero-shot evaluation of 27 retrievers shows that multilingual text retrievers outperform visual retrievers on Arabic by 13.6 nDCG@10, except on layout-rich news pages. DEAM establishes a public foundation for Arabic visual document retrieval.https://huggingface.co/collections/NAMAA-Space/deam-for-arabic-multimodal-retrieval"
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<abstract>Modern visual document retrieval (VDR) models remain strongly English-centric, leaving Arabic substantially under-served in this rapidly advancing field. To address this gap, we introduce DEAM, the first open Arabic visual document retrieval dataset explicitly designed for fine-tuning and evaluating multimodal retrievers on Arabic. DEAM is built through a human-in-the-loop construction pipeline with annotators community annotators, combining Arabic document rendering, query creation, validation, and retrieval-oriented hard-negative mining. The dataset comprises18,275 rendered Arabic documents spanning nine source domains and 82,727 contrastive retrieval triplets, each including five mined hard negatives and one easy negative for contrastive fine-tuning. Zero-shot evaluation of 27 retrievers shows that multilingual text retrievers outperform visual retrievers on Arabic by 13.6 nDCG@10, except on layout-rich news pages. DEAM establishes a public foundation for Arabic visual document retrieval.https://huggingface.co/collections/NAMAA-Space/deam-for-arabic-multimodal-retrieval</abstract>
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%0 Conference Proceedings
%T DEAM: Diverse Embeddings for Arabic Multimodal Retrieval
%A Nacar, Omer
%A Mohamed Emad Elden, Fatimah
%A Al Hallay, Rahaf Saeed
%A A.Azim, Mona
%A Hamod, Aya
%A Zaytoon, Mohamed
%A Qandos, Nour
%A Abdelaziz, Mohamed
%A Elhosiny, Israa
%A El-Ghawi, Yousra Alaa
%A Sibaee, Serry
%A Hamoud, Hadi
%A Al-Zaid, Norah Naji
%A Ragab, Muhammed
%A Al Jallad, Khloud
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F nacar-etal-2026-deam
%X Modern visual document retrieval (VDR) models remain strongly English-centric, leaving Arabic substantially under-served in this rapidly advancing field. To address this gap, we introduce DEAM, the first open Arabic visual document retrieval dataset explicitly designed for fine-tuning and evaluating multimodal retrievers on Arabic. DEAM is built through a human-in-the-loop construction pipeline with annotators community annotators, combining Arabic document rendering, query creation, validation, and retrieval-oriented hard-negative mining. The dataset comprises18,275 rendered Arabic documents spanning nine source domains and 82,727 contrastive retrieval triplets, each including five mined hard negatives and one easy negative for contrastive fine-tuning. Zero-shot evaluation of 27 retrievers shows that multilingual text retrievers outperform visual retrievers on Arabic by 13.6 nDCG@10, except on layout-rich news pages. DEAM establishes a public foundation for Arabic visual document retrieval.https://huggingface.co/collections/NAMAA-Space/deam-for-arabic-multimodal-retrieval
%U https://aclanthology.org/2026.emnlp-main.2329/
%P 52897-52913
Markdown (Informal)
[DEAM: Diverse Embeddings for Arabic Multimodal Retrieval](https://aclanthology.org/2026.emnlp-main.2329/) (Nacar et al., EMNLP 2026)
ACL
- Omer Nacar, Fatimah Mohamed Emad Elden, Rahaf Saeed Al Hallay, Mona A.Azim, Aya Hamod, Mohamed Zaytoon, Nour Qandos, Mohamed Abdelaziz, Israa Elhosiny, Yousra Alaa El-Ghawi, Serry Sibaee, Hadi Hamoud, Norah Naji Al-Zaid, Muhammed Ragab, and Khloud Al Jallad. 2026. DEAM: Diverse Embeddings for Arabic Multimodal Retrieval. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 52897–52913, Budapest, Hungary. Association for Computational Linguistics.