@inproceedings{barmandah-etal-2026-namaa-dialectsenteval,
title = "{NAMAA} at {D}ialect{S}ent{E}val 2026 Subtask 2: From Reranked Sampling to Cross-Backbone Ensembling for {A}rabic Dialectal Sentiment Swap",
author = "Barmandah, Hassan and
El-Ghawi, Yousra Alaa and
A.Azim, Mona and
Nacar, Omer",
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.182/",
pages = "1436--1440",
ISBN = "979-8-89176-497-2",
abstract = "Arabic sentiment swap aims to reverse the polarity of a dialectal sentence while preserving its underlying meaning. We present NAMAA{'}s system for DialectSentEval 2026 Subtask{~}2. Fine-tuning AraT5v2 achieves 79.56 development sentiment-style accuracy, while reranked sampling over eight candidates, using sentiment agreement and content-overlap signals, raises this to 93.57. Cross-backbone ensembling further reaches a development score of 96.78. On the hidden test set, however, this ordering does not transfer: a widened rerank pool with a re-tuned content weight achieves our best official score of 0.7597, whereas the most complex all-source ensemble scores 0.7343. We identify two likely contributors to this development-to-test mismatch: the absence of gold source-polarity labels at test time, which requires classifier-based polarity inference, and train{--}development sentence duplication that may inflate development performance. These results show that additional generation and ensemble complexity can improve development metrics substantially without guaranteeing better generalization under shifted evaluation conditions."
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<abstract>Arabic sentiment swap aims to reverse the polarity of a dialectal sentence while preserving its underlying meaning. We present NAMAA’s system for DialectSentEval 2026 Subtask 2. Fine-tuning AraT5v2 achieves 79.56 development sentiment-style accuracy, while reranked sampling over eight candidates, using sentiment agreement and content-overlap signals, raises this to 93.57. Cross-backbone ensembling further reaches a development score of 96.78. On the hidden test set, however, this ordering does not transfer: a widened rerank pool with a re-tuned content weight achieves our best official score of 0.7597, whereas the most complex all-source ensemble scores 0.7343. We identify two likely contributors to this development-to-test mismatch: the absence of gold source-polarity labels at test time, which requires classifier-based polarity inference, and train–development sentence duplication that may inflate development performance. These results show that additional generation and ensemble complexity can improve development metrics substantially without guaranteeing better generalization under shifted evaluation conditions.</abstract>
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%0 Conference Proceedings
%T NAMAA at DialectSentEval 2026 Subtask 2: From Reranked Sampling to Cross-Backbone Ensembling for Arabic Dialectal Sentiment Swap
%A Barmandah, Hassan
%A El-Ghawi, Yousra Alaa
%A A.Azim, Mona
%A Nacar, Omer
%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 barmandah-etal-2026-namaa-dialectsenteval
%X Arabic sentiment swap aims to reverse the polarity of a dialectal sentence while preserving its underlying meaning. We present NAMAA’s system for DialectSentEval 2026 Subtask 2. Fine-tuning AraT5v2 achieves 79.56 development sentiment-style accuracy, while reranked sampling over eight candidates, using sentiment agreement and content-overlap signals, raises this to 93.57. Cross-backbone ensembling further reaches a development score of 96.78. On the hidden test set, however, this ordering does not transfer: a widened rerank pool with a re-tuned content weight achieves our best official score of 0.7597, whereas the most complex all-source ensemble scores 0.7343. We identify two likely contributors to this development-to-test mismatch: the absence of gold source-polarity labels at test time, which requires classifier-based polarity inference, and train–development sentence duplication that may inflate development performance. These results show that additional generation and ensemble complexity can improve development metrics substantially without guaranteeing better generalization under shifted evaluation conditions.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.182/
%P 1436-1440
Markdown (Informal)
[NAMAA at DialectSentEval 2026 Subtask 2: From Reranked Sampling to Cross-Backbone Ensembling for Arabic Dialectal Sentiment Swap](https://aclanthology.org/2026.arabicnlp-sharedtasks.182/) (Barmandah et al., ArabicNLP 2026)
ACL