@inproceedings{akarrouch-2026-akarrouch,
title = "{AKARROUCH} at {D}ialect{S}ent{E}val 2026: A Generate-and-Rerank Approach for {A}rabic Dialect Sentiment Swapping",
author = "Akarrouch, Mohamed",
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.174/",
pages = "1394--1397",
ISBN = "979-8-89176-497-2",
abstract = "We describe the AKARROUCH submission to DialectSentEval 2026 Subtask 2, which requires reversing the sentiment of an Arabic dialect sentence while preserving its content. We fine-tune AraT5v2 for direction-controlled generation and use it to produce multiple candidates. We then rerank these candidates using CAMeLBERT target-sentiment probabilities and source-to-candidate chrF. The system ranked second in the official evaluation, achieving 78.51{\%} sentiment accuracy, 49.82 BLEU, and 70.12 chrF."
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<abstract>We describe the AKARROUCH submission to DialectSentEval 2026 Subtask 2, which requires reversing the sentiment of an Arabic dialect sentence while preserving its content. We fine-tune AraT5v2 for direction-controlled generation and use it to produce multiple candidates. We then rerank these candidates using CAMeLBERT target-sentiment probabilities and source-to-candidate chrF. The system ranked second in the official evaluation, achieving 78.51% sentiment accuracy, 49.82 BLEU, and 70.12 chrF.</abstract>
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%0 Conference Proceedings %T AKARROUCH at DialectSentEval 2026: A Generate-and-Rerank Approach for Arabic Dialect Sentiment Swapping %A Akarrouch, Mohamed %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 akarrouch-2026-akarrouch %X We describe the AKARROUCH submission to DialectSentEval 2026 Subtask 2, which requires reversing the sentiment of an Arabic dialect sentence while preserving its content. We fine-tune AraT5v2 for direction-controlled generation and use it to produce multiple candidates. We then rerank these candidates using CAMeLBERT target-sentiment probabilities and source-to-candidate chrF. The system ranked second in the official evaluation, achieving 78.51% sentiment accuracy, 49.82 BLEU, and 70.12 chrF. %U https://aclanthology.org/2026.arabicnlp-sharedtasks.174/ %P 1394-1397
Markdown (Informal)
[AKARROUCH at DialectSentEval 2026: A Generate-and-Rerank Approach for Arabic Dialect Sentiment Swapping](https://aclanthology.org/2026.arabicnlp-sharedtasks.174/) (Akarrouch, ArabicNLP 2026)