@inproceedings{abdallah-el-beltagy-2026-lahjamt,
title = "{L}ahja{MT} at {A}lexandria{X}-2026: A Context-Aware {E}nglish-to-Dialectal {A}rabic Machine Translation System with Lightweight Routing of {L}o{RA} Experts",
author = "Abdallah, Mohamed and
El-Beltagy, Samhaa R.",
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.107/",
pages = "857--863",
ISBN = "979-8-89176-497-2",
abstract = "Dialectal Arabic dialogue translation requires modeling both regional variation and conversational context under uneven supervision. We introduce LahjaMT, a compact system for translating English dialogue into thirteen country-level Arabic varieties. LahjaMT adapts the 3.12B-parameter NileChat-3B-Base model with LoRA, conditions translation on dialogue history and metadata, and routes each dialect to a checkpoint{--}prompt expert selected on the held-out selection set. The constrained system shares one backbone across all varieties; the unconstrained system changes only the Libyan and Sudanese routes, replacing them with specialists trained on auxiliary data. LahjaMT ranked second in the constrained track with 28.30 spBLEU and third in the unconstrained track with 28.54 spBLEU."
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<abstract>Dialectal Arabic dialogue translation requires modeling both regional variation and conversational context under uneven supervision. We introduce LahjaMT, a compact system for translating English dialogue into thirteen country-level Arabic varieties. LahjaMT adapts the 3.12B-parameter NileChat-3B-Base model with LoRA, conditions translation on dialogue history and metadata, and routes each dialect to a checkpoint–prompt expert selected on the held-out selection set. The constrained system shares one backbone across all varieties; the unconstrained system changes only the Libyan and Sudanese routes, replacing them with specialists trained on auxiliary data. LahjaMT ranked second in the constrained track with 28.30 spBLEU and third in the unconstrained track with 28.54 spBLEU.</abstract>
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%0 Conference Proceedings
%T LahjaMT at AlexandriaX-2026: A Context-Aware English-to-Dialectal Arabic Machine Translation System with Lightweight Routing of LoRA Experts
%A Abdallah, Mohamed
%A El-Beltagy, Samhaa R.
%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 abdallah-el-beltagy-2026-lahjamt
%X Dialectal Arabic dialogue translation requires modeling both regional variation and conversational context under uneven supervision. We introduce LahjaMT, a compact system for translating English dialogue into thirteen country-level Arabic varieties. LahjaMT adapts the 3.12B-parameter NileChat-3B-Base model with LoRA, conditions translation on dialogue history and metadata, and routes each dialect to a checkpoint–prompt expert selected on the held-out selection set. The constrained system shares one backbone across all varieties; the unconstrained system changes only the Libyan and Sudanese routes, replacing them with specialists trained on auxiliary data. LahjaMT ranked second in the constrained track with 28.30 spBLEU and third in the unconstrained track with 28.54 spBLEU.
%U https://aclanthology.org/2026.arabicnlp-sharedtasks.107/
%P 857-863
Markdown (Informal)
[LahjaMT at AlexandriaX-2026: A Context-Aware English-to-Dialectal Arabic Machine Translation System with Lightweight Routing of LoRA Experts](https://aclanthology.org/2026.arabicnlp-sharedtasks.107/) (Abdallah & El-Beltagy, ArabicNLP 2026)
ACL