@inproceedings{bahgat-etal-2026-nilenlp,
title = "{N}ile{NLP} at {D}ialect{S}ent{E}val 2026: {SWAPPER} Sentiment {SWAP}ping with Parameter-Effcient adapte{R}s for {A}rabic Dialects",
author = "Bahgat, Mohamed and
Alshrief, Mahmoud and
Elhossiny, Ehab Ashraf Mohamed and
Elashry, Abdelgwad",
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.175/",
pages = "1398--1402",
ISBN = "979-8-89176-497-2",
abstract = "We describe our participation in Subtask 2 of the DialectSentEval 2026 Shared Task, whose goal is to rewrite an Arabic dialectal sentence so its sentiment polarity is reversed while its meaning, entities, and dialect are preserved. Our system, SWAPPER (Sentiment-SWAPping with Parameter-Efficient adapteRs), is an ensemble of LoRA-adapted mBART-50 checkpoints in which the adapters are extended beyond attention into the feed-forward sub-layers, where the actual lexical rewriting is learned. Three design choices carried most of the weight: (i) a minimal Arabic instruction prefix that reserves encoder capacity for content; (ii) feed-forward LoRA; (iii) forcing the decoder{'}s beginning-of-sequence token to ar{\_}AR so mBART-50 does not drift into other scripts. On the official blind test SWAPPER reaches Sentiment-Style Accuracy 0.6732, BLEU 41.38, and chrF 65.84. As a secondary contribution, we introduce the J-Gap ($\Delta J = J_{tgt} - J_{src}$), a Jaccard-based copy-collapse diagnostic that cleanly separates genuine sentiment swappers from models that merely copy their input, and explains why superficially strong backbones such as AraT5v2 fail the task despite high surface metrics."
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<abstract>We describe our participation in Subtask 2 of the DialectSentEval 2026 Shared Task, whose goal is to rewrite an Arabic dialectal sentence so its sentiment polarity is reversed while its meaning, entities, and dialect are preserved. Our system, SWAPPER (Sentiment-SWAPping with Parameter-Efficient adapteRs), is an ensemble of LoRA-adapted mBART-50 checkpoints in which the adapters are extended beyond attention into the feed-forward sub-layers, where the actual lexical rewriting is learned. Three design choices carried most of the weight: (i) a minimal Arabic instruction prefix that reserves encoder capacity for content; (ii) feed-forward LoRA; (iii) forcing the decoder’s beginning-of-sequence token to ar_AR so mBART-50 does not drift into other scripts. On the official blind test SWAPPER reaches Sentiment-Style Accuracy 0.6732, BLEU 41.38, and chrF 65.84. As a secondary contribution, we introduce the J-Gap (Δ J = J_tgt - J_src), a Jaccard-based copy-collapse diagnostic that cleanly separates genuine sentiment swappers from models that merely copy their input, and explains why superficially strong backbones such as AraT5v2 fail the task despite high surface metrics.</abstract>
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%0 Conference Proceedings
%T NileNLP at DialectSentEval 2026: SWAPPER Sentiment SWAPping with Parameter-Effcient adapteRs for Arabic Dialects
%A Bahgat, Mohamed
%A Alshrief, Mahmoud
%A Elhossiny, Ehab Ashraf Mohamed
%A Elashry, Abdelgwad
%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 bahgat-etal-2026-nilenlp
%X We describe our participation in Subtask 2 of the DialectSentEval 2026 Shared Task, whose goal is to rewrite an Arabic dialectal sentence so its sentiment polarity is reversed while its meaning, entities, and dialect are preserved. Our system, SWAPPER (Sentiment-SWAPping with Parameter-Efficient adapteRs), is an ensemble of LoRA-adapted mBART-50 checkpoints in which the adapters are extended beyond attention into the feed-forward sub-layers, where the actual lexical rewriting is learned. Three design choices carried most of the weight: (i) a minimal Arabic instruction prefix that reserves encoder capacity for content; (ii) feed-forward LoRA; (iii) forcing the decoder’s beginning-of-sequence token to ar_AR so mBART-50 does not drift into other scripts. On the official blind test SWAPPER reaches Sentiment-Style Accuracy 0.6732, BLEU 41.38, and chrF 65.84. As a secondary contribution, we introduce the J-Gap (Δ J = J_tgt - J_src), a Jaccard-based copy-collapse diagnostic that cleanly separates genuine sentiment swappers from models that merely copy their input, and explains why superficially strong backbones such as AraT5v2 fail the task despite high surface metrics.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.175/
%P 1398-1402
Markdown (Informal)
[NileNLP at DialectSentEval 2026: SWAPPER Sentiment SWAPping with Parameter-Effcient adapteRs for Arabic Dialects](https://aclanthology.org/2026.arabicnlp-sharedtasks.175/) (Bahgat et al., ArabicNLP 2026)
ACL