@inproceedings{sabaa-sabaa-2026-sabaa-dialectsenteval,
title = "Sabaa at {D}ialect{S}ent{E}val Shared Task Subtask-2: Classifier-Guided Candidate Reranking for {A}rabic Dialectal Sentiment Swap",
author = "Sabaa, Mohamed and
Sabaa, Amr",
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.173/",
pages = "1389--1393",
ISBN = "979-8-89176-497-2",
abstract = "Sentiment swap asks a system to rewrite a sentence so that its polarity is inverted while everything else stays the same, two halves of an instruction that pull against each other. This paper describes how the Sabaa system navigates that trade-off for Arabic dialects in Subtask 2 of DialectSentEval 2026. Rather than asking one model for a single correct rewrite, we ask two fine-tuned Arabic sequence-to-sequence models to each propose several, then choose among the pooled proposals with a scoring function built from the signals the task grades us on. The system scores 49.07 BLEU, 69.33 chrF, and 0.7447 sentiment accuracy on the official test set. That last figure is the most informative result here: the system places 90.37{\%} of its outputs at the polarity it was aiming for, and the shortfall traces to inferring each sentence{'}s original polarity, which the test data withholds. Measured against gold labels on development, that inference is correct only 74.07{\%} of the time, and sixteen points of sentiment accuracy separate our submission from a perfect estimator."
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<abstract>Sentiment swap asks a system to rewrite a sentence so that its polarity is inverted while everything else stays the same, two halves of an instruction that pull against each other. This paper describes how the Sabaa system navigates that trade-off for Arabic dialects in Subtask 2 of DialectSentEval 2026. Rather than asking one model for a single correct rewrite, we ask two fine-tuned Arabic sequence-to-sequence models to each propose several, then choose among the pooled proposals with a scoring function built from the signals the task grades us on. The system scores 49.07 BLEU, 69.33 chrF, and 0.7447 sentiment accuracy on the official test set. That last figure is the most informative result here: the system places 90.37% of its outputs at the polarity it was aiming for, and the shortfall traces to inferring each sentence’s original polarity, which the test data withholds. Measured against gold labels on development, that inference is correct only 74.07% of the time, and sixteen points of sentiment accuracy separate our submission from a perfect estimator.</abstract>
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%0 Conference Proceedings
%T Sabaa at DialectSentEval Shared Task Subtask-2: Classifier-Guided Candidate Reranking for Arabic Dialectal Sentiment Swap
%A Sabaa, Mohamed
%A Sabaa, Amr
%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 sabaa-sabaa-2026-sabaa-dialectsenteval
%X Sentiment swap asks a system to rewrite a sentence so that its polarity is inverted while everything else stays the same, two halves of an instruction that pull against each other. This paper describes how the Sabaa system navigates that trade-off for Arabic dialects in Subtask 2 of DialectSentEval 2026. Rather than asking one model for a single correct rewrite, we ask two fine-tuned Arabic sequence-to-sequence models to each propose several, then choose among the pooled proposals with a scoring function built from the signals the task grades us on. The system scores 49.07 BLEU, 69.33 chrF, and 0.7447 sentiment accuracy on the official test set. That last figure is the most informative result here: the system places 90.37% of its outputs at the polarity it was aiming for, and the shortfall traces to inferring each sentence’s original polarity, which the test data withholds. Measured against gold labels on development, that inference is correct only 74.07% of the time, and sixteen points of sentiment accuracy separate our submission from a perfect estimator.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.173/
%P 1389-1393
Markdown (Informal)
[Sabaa at DialectSentEval Shared Task Subtask-2: Classifier-Guided Candidate Reranking for Arabic Dialectal Sentiment Swap](https://aclanthology.org/2026.arabicnlp-sharedtasks.173/) (Sabaa & Sabaa, ArabicNLP 2026)
ACL