@inproceedings{khaled-etal-2026-nu,
title = "{NU}-{S}ent{E}val at {D}ialect{S}ent{E}val Shared Task Subtask-2: Fine-Tuning {A}ra{T}5 for {A}rabic Dialect Sentiment Swap",
author = "Khaled, Areej and
Mazen, Maraim and
Tamer, Alaa and
Mohamed, Ensaf H.",
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.185/",
pages = "1452--1456",
ISBN = "979-8-89176-497-2",
abstract = "We present NU-SentEval{'}s system for DialectSentEval 2026 Subtask 2 (Arabic Dialect Sentiment Swap), which rewrites Arabic dialectal sentences into the opposite sentiment polarity while preserving their core content. Our approach fine-tunes AraT5v2-base-1024, a pretrained Arabic sequence-to-sequence model, directly on the shared task{'}s source-target sentence pairs, and generates outputs using standard beam search decoding. On the official Final Evaluation test leaderboard, our system achieves 54.63 BLEU, 72.61 chrF, and 68.98{\%} sentiment preservation. Our manual error analysis shows the model reliably inverts sentiment for short, single-clause inputs, often matching the human reference exactly, though it occasionally fails to propagate the sentiment flip to secondary cues such as emojis, resulting in mixed-signal outputs."
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<abstract>We present NU-SentEval’s system for DialectSentEval 2026 Subtask 2 (Arabic Dialect Sentiment Swap), which rewrites Arabic dialectal sentences into the opposite sentiment polarity while preserving their core content. Our approach fine-tunes AraT5v2-base-1024, a pretrained Arabic sequence-to-sequence model, directly on the shared task’s source-target sentence pairs, and generates outputs using standard beam search decoding. On the official Final Evaluation test leaderboard, our system achieves 54.63 BLEU, 72.61 chrF, and 68.98% sentiment preservation. Our manual error analysis shows the model reliably inverts sentiment for short, single-clause inputs, often matching the human reference exactly, though it occasionally fails to propagate the sentiment flip to secondary cues such as emojis, resulting in mixed-signal outputs.</abstract>
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%0 Conference Proceedings
%T NU-SentEval at DialectSentEval Shared Task Subtask-2: Fine-Tuning AraT5 for Arabic Dialect Sentiment Swap
%A Khaled, Areej
%A Mazen, Maraim
%A Tamer, Alaa
%A Mohamed, Ensaf H.
%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 khaled-etal-2026-nu
%X We present NU-SentEval’s system for DialectSentEval 2026 Subtask 2 (Arabic Dialect Sentiment Swap), which rewrites Arabic dialectal sentences into the opposite sentiment polarity while preserving their core content. Our approach fine-tunes AraT5v2-base-1024, a pretrained Arabic sequence-to-sequence model, directly on the shared task’s source-target sentence pairs, and generates outputs using standard beam search decoding. On the official Final Evaluation test leaderboard, our system achieves 54.63 BLEU, 72.61 chrF, and 68.98% sentiment preservation. Our manual error analysis shows the model reliably inverts sentiment for short, single-clause inputs, often matching the human reference exactly, though it occasionally fails to propagate the sentiment flip to secondary cues such as emojis, resulting in mixed-signal outputs.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.185/
%P 1452-1456
Markdown (Informal)
[NU-SentEval at DialectSentEval Shared Task Subtask-2: Fine-Tuning AraT5 for Arabic Dialect Sentiment Swap](https://aclanthology.org/2026.arabicnlp-sharedtasks.185/) (Khaled et al., ArabicNLP 2026)
ACL