@inproceedings{almoustafa-matsuzaki-2026-lahjasensei,
title = "{L}ahja{S}ensei at {D}ialect{S}ent{E}val 2026: Few-Shot Prompting and Model Merging for Sentiment Swap",
author = "Almoustafa, Ahmad and
Matsuzaki, Takuya",
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.176/",
pages = "1403--1408",
ISBN = "979-8-89176-497-2",
abstract = "Arabic dialect sentiment swapping requires systems to reverse the affective orientation of an utterance while preserving its core meaning, dialectal form, and fluency. This task is particularly challenging because sentiment may be conveyed through dialect-specific vocabulary, negation patterns, and spelling variation. We present LahjaSensei, our system for Subtask 2 of the DialectSentEval shared task. We investigate two complementary approaches: prompt-based generation with GPT-5.6 and parameter-efficient fine-tuning of Qwen2.5-7B. We evaluate GPT-5.6 under zero-shot, retrieved few-shot, stronger Arabic instructions, and direction-aware configurations. The direction-aware GPT-5.6 pipeline explicitly predicts source polarity before retrieving direction-matched demonstrations and generating the rewrite. For Qwen2.5-7B, we compare pooled QLoRA with linear and TIES merges of direction-specific adapters. Direction-aware GPT-5.6 prompting achieves the highest test sentiment accuracy and remains stable across development and test. Pooled QLoRA produces the highest development BLEU and chrF, while linear merging obtains the highest test BLEU and chrF but lower sentiment accuracy. These findings highlight the central trade-off between sentiment control and preservation of semantic content and demonstrate the value of explicit direction modeling for Arabic dialect sentiment swapping."
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<abstract>Arabic dialect sentiment swapping requires systems to reverse the affective orientation of an utterance while preserving its core meaning, dialectal form, and fluency. This task is particularly challenging because sentiment may be conveyed through dialect-specific vocabulary, negation patterns, and spelling variation. We present LahjaSensei, our system for Subtask 2 of the DialectSentEval shared task. We investigate two complementary approaches: prompt-based generation with GPT-5.6 and parameter-efficient fine-tuning of Qwen2.5-7B. We evaluate GPT-5.6 under zero-shot, retrieved few-shot, stronger Arabic instructions, and direction-aware configurations. The direction-aware GPT-5.6 pipeline explicitly predicts source polarity before retrieving direction-matched demonstrations and generating the rewrite. For Qwen2.5-7B, we compare pooled QLoRA with linear and TIES merges of direction-specific adapters. Direction-aware GPT-5.6 prompting achieves the highest test sentiment accuracy and remains stable across development and test. Pooled QLoRA produces the highest development BLEU and chrF, while linear merging obtains the highest test BLEU and chrF but lower sentiment accuracy. These findings highlight the central trade-off between sentiment control and preservation of semantic content and demonstrate the value of explicit direction modeling for Arabic dialect sentiment swapping.</abstract>
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%0 Conference Proceedings
%T LahjaSensei at DialectSentEval 2026: Few-Shot Prompting and Model Merging for Sentiment Swap
%A Almoustafa, Ahmad
%A Matsuzaki, Takuya
%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 almoustafa-matsuzaki-2026-lahjasensei
%X Arabic dialect sentiment swapping requires systems to reverse the affective orientation of an utterance while preserving its core meaning, dialectal form, and fluency. This task is particularly challenging because sentiment may be conveyed through dialect-specific vocabulary, negation patterns, and spelling variation. We present LahjaSensei, our system for Subtask 2 of the DialectSentEval shared task. We investigate two complementary approaches: prompt-based generation with GPT-5.6 and parameter-efficient fine-tuning of Qwen2.5-7B. We evaluate GPT-5.6 under zero-shot, retrieved few-shot, stronger Arabic instructions, and direction-aware configurations. The direction-aware GPT-5.6 pipeline explicitly predicts source polarity before retrieving direction-matched demonstrations and generating the rewrite. For Qwen2.5-7B, we compare pooled QLoRA with linear and TIES merges of direction-specific adapters. Direction-aware GPT-5.6 prompting achieves the highest test sentiment accuracy and remains stable across development and test. Pooled QLoRA produces the highest development BLEU and chrF, while linear merging obtains the highest test BLEU and chrF but lower sentiment accuracy. These findings highlight the central trade-off between sentiment control and preservation of semantic content and demonstrate the value of explicit direction modeling for Arabic dialect sentiment swapping.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.176/
%P 1403-1408
Markdown (Informal)
[LahjaSensei at DialectSentEval 2026: Few-Shot Prompting and Model Merging for Sentiment Swap](https://aclanthology.org/2026.arabicnlp-sharedtasks.176/) (Almoustafa & Matsuzaki, ArabicNLP 2026)
ACL