@inproceedings{ben-chaabane-etal-2026-anlp,
title = "{ANLP}-{U}ni{S}o at {A}r{G}uard 2026 Task A: Multimodal {A}ra{BERT}{--}{R}es{N}et50 with Knowledge Graph Fusion for {A}rabic Hateful Meme Detection",
author = "Ben Chaabane, Saoussen and
Trigui, Omar and
Jaoua, Maher",
editor = "Abdul-Mageed, Muhammad and
Elsayed, Tamer and
Bouamor, Houda and
El-Beltagy, Samhaa and
Al-Khalifa, Hend and
Abdelali, Ahmed and
Elmadany, AbdelRahim and
El Mekki, Abdellah and
Darwish, Kareem and
Alkhereyf, Sakhar and
Luqman, Hamzah and
Khalifa, Salam and
Magdy, Samar and
Alyafeai, Zaid and
Hamed, Injy and
Zaghouani, Wajdi and
Zitouni, Imed",
booktitle = "Proceedings of The Fourth {A}rabic Natural Language Processing Conference: Shared Tasks",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.arabicnlp-sharedtasks.58/",
pages = "448--452",
ISBN = "979-8-89176-497-2",
abstract = "Detecting hateful content in Arabic memes is challenging due to dialectal variation, sarcasm, cultural references, and the interplay between visual and textual signals. We participate in ArGuard 2026 Track A (Multimodal Hateful Meme Detection) with a multimodal architecture that fuses AraBERT textual representations, ResNet50 visual features, and a lightweight knowledge-graph (KG) ontology vector derived from domain-specific Arabicbinary keywords. The model jointly optimizes (Hateful / Not Hateful) and multi-label finegrained classification through a gated fusion layer with strong regularization (dropout 0.45, weight decay 0.05, label smoothing 0.1). Gradual unfreezing of AraBERT layers, weighted sampling, and Arabic-specific normalization further improve robustness. On the official development set the system reaches a binary macro-F1 of 0.710; on the blind test set it obtains binary macro-F1 0.717. We analyse the contribution of each modality and discuss remaining challenges of fine-grained multi-label prediction under severe class imbalance."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="ben-chaabane-etal-2026-anlp">
<titleInfo>
<title>ANLP-UniSo at ArGuard 2026 Task A: Multimodal AraBERT–ResNet50 with Knowledge Graph Fusion for Arabic Hateful Meme Detection</title>
</titleInfo>
<name type="personal">
<namePart type="given">Saoussen</namePart>
<namePart type="family">Ben Chaabane</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Omar</namePart>
<namePart type="family">Trigui</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Maher</namePart>
<namePart type="family">Jaoua</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-10</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of The Fourth Arabic Natural Language Processing Conference: Shared Tasks</title>
</titleInfo>
<name type="personal">
<namePart type="given">Muhammad</namePart>
<namePart type="family">Abdul-Mageed</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tamer</namePart>
<namePart type="family">Elsayed</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Houda</namePart>
<namePart type="family">Bouamor</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Samhaa</namePart>
<namePart type="family">El-Beltagy</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hend</namePart>
<namePart type="family">Al-Khalifa</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ahmed</namePart>
<namePart type="family">Abdelali</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">AbdelRahim</namePart>
<namePart type="family">Elmadany</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Abdellah</namePart>
<namePart type="family">El Mekki</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kareem</namePart>
<namePart type="family">Darwish</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sakhar</namePart>
<namePart type="family">Alkhereyf</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hamzah</namePart>
<namePart type="family">Luqman</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Salam</namePart>
<namePart type="family">Khalifa</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Samar</namePart>
<namePart type="family">Magdy</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zaid</namePart>
<namePart type="family">Alyafeai</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Injy</namePart>
<namePart type="family">Hamed</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Wajdi</namePart>
<namePart type="family">Zaghouani</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Imed</namePart>
<namePart type="family">Zitouni</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Budapest, Hungary</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-497-2</identifier>
</relatedItem>
<abstract>Detecting hateful content in Arabic memes is challenging due to dialectal variation, sarcasm, cultural references, and the interplay between visual and textual signals. We participate in ArGuard 2026 Track A (Multimodal Hateful Meme Detection) with a multimodal architecture that fuses AraBERT textual representations, ResNet50 visual features, and a lightweight knowledge-graph (KG) ontology vector derived from domain-specific Arabicbinary keywords. The model jointly optimizes (Hateful / Not Hateful) and multi-label finegrained classification through a gated fusion layer with strong regularization (dropout 0.45, weight decay 0.05, label smoothing 0.1). Gradual unfreezing of AraBERT layers, weighted sampling, and Arabic-specific normalization further improve robustness. On the official development set the system reaches a binary macro-F1 of 0.710; on the blind test set it obtains binary macro-F1 0.717. We analyse the contribution of each modality and discuss remaining challenges of fine-grained multi-label prediction under severe class imbalance.</abstract>
<identifier type="citekey">ben-chaabane-etal-2026-anlp</identifier>
<location>
<url>https://aclanthology.org/2026.arabicnlp-sharedtasks.58/</url>
</location>
<part>
<date>2026-10</date>
<extent unit="page">
<start>448</start>
<end>452</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T ANLP-UniSo at ArGuard 2026 Task A: Multimodal AraBERT–ResNet50 with Knowledge Graph Fusion for Arabic Hateful Meme Detection
%A Ben Chaabane, Saoussen
%A Trigui, Omar
%A Jaoua, Maher
%Y Abdul-Mageed, Muhammad
%Y Elsayed, Tamer
%Y Bouamor, Houda
%Y El-Beltagy, Samhaa
%Y Al-Khalifa, Hend
%Y Abdelali, Ahmed
%Y Elmadany, AbdelRahim
%Y El Mekki, Abdellah
%Y Darwish, Kareem
%Y Alkhereyf, Sakhar
%Y Luqman, Hamzah
%Y Khalifa, Salam
%Y Magdy, Samar
%Y Alyafeai, Zaid
%Y Hamed, Injy
%Y Zaghouani, Wajdi
%Y Zitouni, Imed
%S Proceedings of The Fourth Arabic Natural Language Processing Conference: Shared Tasks
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-497-2
%F ben-chaabane-etal-2026-anlp
%X Detecting hateful content in Arabic memes is challenging due to dialectal variation, sarcasm, cultural references, and the interplay between visual and textual signals. We participate in ArGuard 2026 Track A (Multimodal Hateful Meme Detection) with a multimodal architecture that fuses AraBERT textual representations, ResNet50 visual features, and a lightweight knowledge-graph (KG) ontology vector derived from domain-specific Arabicbinary keywords. The model jointly optimizes (Hateful / Not Hateful) and multi-label finegrained classification through a gated fusion layer with strong regularization (dropout 0.45, weight decay 0.05, label smoothing 0.1). Gradual unfreezing of AraBERT layers, weighted sampling, and Arabic-specific normalization further improve robustness. On the official development set the system reaches a binary macro-F1 of 0.710; on the blind test set it obtains binary macro-F1 0.717. We analyse the contribution of each modality and discuss remaining challenges of fine-grained multi-label prediction under severe class imbalance.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.58/
%P 448-452
Markdown (Informal)
[ANLP-UniSo at ArGuard 2026 Task A: Multimodal AraBERT–ResNet50 with Knowledge Graph Fusion for Arabic Hateful Meme Detection](https://aclanthology.org/2026.arabicnlp-sharedtasks.58/) (Ben Chaabane et al., ArabicNLP 2026)
ACL