@inproceedings{aly-etal-2026-fcds,
title = "{FCDS} at {A}lexandria{X}-2026: Improving Context Aware Dialogue {E}nglish To Dialectal {A}rabic Machine Translation Via Context Strategies And Quality Aware Decoding",
author = "Aly, Ranwa and
Maan, Seif Eldin and
Aly, Farida Mohamed and
Ashraf, Isis",
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.106/",
pages = "848--856",
ISBN = "979-8-89176-497-2",
abstract = "Translating English dialogue into dialectal Arabic depends on the target dialect, conversational context and speaker attributes, and these challenges are compounded by the scarcity of dialectal parallel data. We present our system for the AlexandriaX-2026 Context-Aware English-to-Dialectal Arabic Dialogue Translation constrained track, which restricts participants to the provided Alexandria dataset and models of $\leq$5B parameters. We fine-tune open LLMs, exploring different context composition strategies for training and inference, and find that mismatched strategies can outperform matched ones depending on the model. We further apply quality-aware decoding (QAD) via Minimum Bayes Risk, tuning the utility function and candidate pool size for consistent gains over greedy decoding. Our final system, a fine-tuned Gemma-3-4B-it with spBLEU-based QAD and a pool size of 50 samples, achieves 26.48 spBLEU and 42.3 chrF++, ranking third on the constrained-track leaderboard."
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<abstract>Translating English dialogue into dialectal Arabic depends on the target dialect, conversational context and speaker attributes, and these challenges are compounded by the scarcity of dialectal parallel data. We present our system for the AlexandriaX-2026 Context-Aware English-to-Dialectal Arabic Dialogue Translation constrained track, which restricts participants to the provided Alexandria dataset and models of łeq5B parameters. We fine-tune open LLMs, exploring different context composition strategies for training and inference, and find that mismatched strategies can outperform matched ones depending on the model. We further apply quality-aware decoding (QAD) via Minimum Bayes Risk, tuning the utility function and candidate pool size for consistent gains over greedy decoding. Our final system, a fine-tuned Gemma-3-4B-it with spBLEU-based QAD and a pool size of 50 samples, achieves 26.48 spBLEU and 42.3 chrF++, ranking third on the constrained-track leaderboard.</abstract>
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%0 Conference Proceedings
%T FCDS at AlexandriaX-2026: Improving Context Aware Dialogue English To Dialectal Arabic Machine Translation Via Context Strategies And Quality Aware Decoding
%A Aly, Ranwa
%A Maan, Seif Eldin
%A Aly, Farida Mohamed
%A Ashraf, Isis
%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 aly-etal-2026-fcds
%X Translating English dialogue into dialectal Arabic depends on the target dialect, conversational context and speaker attributes, and these challenges are compounded by the scarcity of dialectal parallel data. We present our system for the AlexandriaX-2026 Context-Aware English-to-Dialectal Arabic Dialogue Translation constrained track, which restricts participants to the provided Alexandria dataset and models of łeq5B parameters. We fine-tune open LLMs, exploring different context composition strategies for training and inference, and find that mismatched strategies can outperform matched ones depending on the model. We further apply quality-aware decoding (QAD) via Minimum Bayes Risk, tuning the utility function and candidate pool size for consistent gains over greedy decoding. Our final system, a fine-tuned Gemma-3-4B-it with spBLEU-based QAD and a pool size of 50 samples, achieves 26.48 spBLEU and 42.3 chrF++, ranking third on the constrained-track leaderboard.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.106/
%P 848-856
Markdown (Informal)
[FCDS at AlexandriaX-2026: Improving Context Aware Dialogue English To Dialectal Arabic Machine Translation Via Context Strategies And Quality Aware Decoding](https://aclanthology.org/2026.arabicnlp-sharedtasks.106/) (Aly et al., ArabicNLP 2026)
ACL