@inproceedings{ezzini-etal-2026-dialectsenteval,
title = "{D}ialect{S}ent{E}val 2026: {A}rabic Dialect Sentiment Analysis and Swapping Shared Task",
author = "Ezzini, Saad and
Abudalfa, Shadi and
Alharbi, Maram and
Chafik, Salmane and
Alatawi, Hind Saleh and
El-Haj, Mo and
Abdelali, Ahmed and
Alnahari, Osamah and
Lamsiyah, Salima",
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.172/",
pages = "1380--1388",
ISBN = "979-8-89176-497-2",
abstract = "Sentiment analysis is a fundamental problem in Natural Language Processing (NLP). Standard sentiment classification for the Arabic language remains challenging due to the high volume of dialectal Arabic. To advance research in this area, this paper proposes the Shared Task on Sentiment Analysis and Swapping in Arabic Dialects (DialectSentEval), hosted with the Arabic Natural Language Processing Conference (ArabicNLP 2026). This shared task consists of two subtasks: Subtask 1 focuses on multi-class and multi-dialect sentiment analysis, requiring models to identify sentiment polarity across various Arabic dialects. Subtask 2 introduces a generative task for Arabic sentiment swap, challenging models to invert sentiment polarity while preserving core semantics. In this overview paper, we present the motivation, dataset creation, and summarize the main findings from participating models."
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<abstract>Sentiment analysis is a fundamental problem in Natural Language Processing (NLP). Standard sentiment classification for the Arabic language remains challenging due to the high volume of dialectal Arabic. To advance research in this area, this paper proposes the Shared Task on Sentiment Analysis and Swapping in Arabic Dialects (DialectSentEval), hosted with the Arabic Natural Language Processing Conference (ArabicNLP 2026). This shared task consists of two subtasks: Subtask 1 focuses on multi-class and multi-dialect sentiment analysis, requiring models to identify sentiment polarity across various Arabic dialects. Subtask 2 introduces a generative task for Arabic sentiment swap, challenging models to invert sentiment polarity while preserving core semantics. In this overview paper, we present the motivation, dataset creation, and summarize the main findings from participating models.</abstract>
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%0 Conference Proceedings
%T DialectSentEval 2026: Arabic Dialect Sentiment Analysis and Swapping Shared Task
%A Ezzini, Saad
%A Abudalfa, Shadi
%A Alharbi, Maram
%A Chafik, Salmane
%A Alatawi, Hind Saleh
%A El-Haj, Mo
%A Abdelali, Ahmed
%A Alnahari, Osamah
%A Lamsiyah, Salima
%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 ezzini-etal-2026-dialectsenteval
%X Sentiment analysis is a fundamental problem in Natural Language Processing (NLP). Standard sentiment classification for the Arabic language remains challenging due to the high volume of dialectal Arabic. To advance research in this area, this paper proposes the Shared Task on Sentiment Analysis and Swapping in Arabic Dialects (DialectSentEval), hosted with the Arabic Natural Language Processing Conference (ArabicNLP 2026). This shared task consists of two subtasks: Subtask 1 focuses on multi-class and multi-dialect sentiment analysis, requiring models to identify sentiment polarity across various Arabic dialects. Subtask 2 introduces a generative task for Arabic sentiment swap, challenging models to invert sentiment polarity while preserving core semantics. In this overview paper, we present the motivation, dataset creation, and summarize the main findings from participating models.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.172/
%P 1380-1388
Markdown (Informal)
[DialectSentEval 2026: Arabic Dialect Sentiment Analysis and Swapping Shared Task](https://aclanthology.org/2026.arabicnlp-sharedtasks.172/) (Ezzini et al., ArabicNLP 2026)
ACL
- Saad Ezzini, Shadi Abudalfa, Maram Alharbi, Salmane Chafik, Hind Saleh Alatawi, Mo El-Haj, Ahmed Abdelali, Osamah Alnahari, and Salima Lamsiyah. 2026. DialectSentEval 2026: Arabic Dialect Sentiment Analysis and Swapping Shared Task. In Proceedings of The Fourth Arabic Natural Language Processing Conference: Shared Tasks, pages 1380–1388, Budapest, Hungary. Association for Computational Linguistics.