@inproceedings{hamdan-etal-2026-u4rasd,
title = "{U}4{RASD} at {S}tance{N}akba Shared Task: Data Augmentation and Auxiliary Objectives for {A}rabic Stance Detection",
author = {Hamdan, Nancy and
Jouni, Aya and
Sa{\"i}d, Aya and
Zaraket, Fadi},
editor = "Jarrar, Mustafa and
El-Haj, Mo and
Haddad, Amal and
Atiani, Serin and
Abudalfa, Shadi and
Regier, Terry and
Rayson, Paul and
Sima{'}an, Khalil and
Mansour, Camille",
booktitle = "Proceedings of the 2nd International Workshop on Nakba Narratives as Language Resources @ {LREC} 2026",
month = may,
year = "2026",
address = "Palma, Mallorca (Spain)",
publisher = "ELRA Language Resources Association (ELRA)",
url = "https://aclanthology.org/2026.nakbanlp-1.29/",
doi = "10.63317/2dsxtdvvu8vu",
pages = "206--211",
abstract = "This paper describes a submission to Track B of the StanceNakba Shared Task on Arabic cross-topic stance detection in the political domain. We investigate LLM-based data augmentation, auxiliary training objectives including contrastive and multi-task learning, zero-shot prompting, and a preliminary terminology-based clustering approach. Our final system, based on MARBERTv2 with dialect-aware LLM-based augmentation, achieved 86{\%} macro-F1 on the blind test set and ranked 3rd out of 10 teams. Our results show that dialect-aware augmentation substantially improved performance in a low-resource Arabic stance detection setting, while not all auxiliary objectives or clustering-based strategies yielded consistent gains. We release our code at \url{https://acr.ps/1L9B9Tw}."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="hamdan-etal-2026-u4rasd">
<titleInfo>
<title>U4RASD at StanceNakba Shared Task: Data Augmentation and Auxiliary Objectives for Arabic Stance Detection</title>
</titleInfo>
<name type="personal">
<namePart type="given">Nancy</namePart>
<namePart type="family">Hamdan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Aya</namePart>
<namePart type="family">Jouni</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Aya</namePart>
<namePart type="family">Saïd</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Fadi</namePart>
<namePart type="family">Zaraket</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-05</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 2nd International Workshop on Nakba Narratives as Language Resources @ LREC 2026</title>
</titleInfo>
<name type="personal">
<namePart type="given">Mustafa</namePart>
<namePart type="family">Jarrar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mo</namePart>
<namePart type="family">El-Haj</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Amal</namePart>
<namePart type="family">Haddad</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Serin</namePart>
<namePart type="family">Atiani</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Shadi</namePart>
<namePart type="family">Abudalfa</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Terry</namePart>
<namePart type="family">Regier</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Paul</namePart>
<namePart type="family">Rayson</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Khalil</namePart>
<namePart type="family">Sima’an</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Camille</namePart>
<namePart type="family">Mansour</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>ELRA Language Resources Association (ELRA)</publisher>
<place>
<placeTerm type="text">Palma, Mallorca (Spain)</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>This paper describes a submission to Track B of the StanceNakba Shared Task on Arabic cross-topic stance detection in the political domain. We investigate LLM-based data augmentation, auxiliary training objectives including contrastive and multi-task learning, zero-shot prompting, and a preliminary terminology-based clustering approach. Our final system, based on MARBERTv2 with dialect-aware LLM-based augmentation, achieved 86% macro-F1 on the blind test set and ranked 3rd out of 10 teams. Our results show that dialect-aware augmentation substantially improved performance in a low-resource Arabic stance detection setting, while not all auxiliary objectives or clustering-based strategies yielded consistent gains. We release our code at https://acr.ps/1L9B9Tw.</abstract>
<identifier type="citekey">hamdan-etal-2026-u4rasd</identifier>
<identifier type="doi">10.63317/2dsxtdvvu8vu</identifier>
<location>
<url>https://aclanthology.org/2026.nakbanlp-1.29/</url>
</location>
<part>
<date>2026-05</date>
<extent unit="page">
<start>206</start>
<end>211</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T U4RASD at StanceNakba Shared Task: Data Augmentation and Auxiliary Objectives for Arabic Stance Detection
%A Hamdan, Nancy
%A Jouni, Aya
%A Saïd, Aya
%A Zaraket, Fadi
%Y Jarrar, Mustafa
%Y El-Haj, Mo
%Y Haddad, Amal
%Y Atiani, Serin
%Y Abudalfa, Shadi
%Y Regier, Terry
%Y Rayson, Paul
%Y Sima’an, Khalil
%Y Mansour, Camille
%S Proceedings of the 2nd International Workshop on Nakba Narratives as Language Resources @ LREC 2026
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F hamdan-etal-2026-u4rasd
%X This paper describes a submission to Track B of the StanceNakba Shared Task on Arabic cross-topic stance detection in the political domain. We investigate LLM-based data augmentation, auxiliary training objectives including contrastive and multi-task learning, zero-shot prompting, and a preliminary terminology-based clustering approach. Our final system, based on MARBERTv2 with dialect-aware LLM-based augmentation, achieved 86% macro-F1 on the blind test set and ranked 3rd out of 10 teams. Our results show that dialect-aware augmentation substantially improved performance in a low-resource Arabic stance detection setting, while not all auxiliary objectives or clustering-based strategies yielded consistent gains. We release our code at https://acr.ps/1L9B9Tw.
%R 10.63317/2dsxtdvvu8vu
%U https://aclanthology.org/2026.nakbanlp-1.29/
%U https://doi.org/10.63317/2dsxtdvvu8vu
%P 206-211
Markdown (Informal)
[U4RASD at StanceNakba Shared Task: Data Augmentation and Auxiliary Objectives for Arabic Stance Detection](https://aclanthology.org/2026.nakbanlp-1.29/) (Hamdan et al., NakbaNLP 2026)
ACL