The paper introduces AraSEG, a new Arabic sentence segmentation corpus covering eight genres and diverse punctuation and document structures. Experiments using AraSEG evaluate large language models, lightweight encoders, and dependency parser-based models, revealing that lightweight encoders and parser-based models outperform LLMs under the most challenging conditions. The study also shows that increasing training data size and genre diversity eventually saturates performance, that cross‑genre generalization remains difficult, and that accurate sentence segmentation significantly improves downstream dependency parsing.
By Mohammed Elkholy, Khalid N. Elmadani, Nizar Habash, Bashar Alhafni
arXiv:2606. 12708v1 Announce Type: cross Abstract: Despite their linguistic diversity and global significance, African languages remain underrepresented in research and resources to support NLP.
By Happy Buzaaba, Cheikh Mouhamadou Bamba Dione, David Ifeoluwa Adelani, Sylvain Kahane, Kim Gerdes, Bruno Guillaume, Kevin Guan, Aremu Anuoluwapo, Naome A. Etori, Shamsuddeen Hassan Muhammad, Utitofon Inyang, Peter Nabende, David Sabiiti Bamutura, Andiswa Bukula, Chinedu Uchechukwu, Rooweither Mabuya, Idris Akinade, Christiane Fellbaum
The paper evaluates large language models (LLMs) on Arabic morphosyntactic tagging and dependency parsing, a challenging task due to rich morphology and orthographic ambiguity. It compares zero‑shot prompting with retrieval‑based in‑context learning across pre‑tokenized, raw‑text, and cascaded settings, finding that relevant demonstrations significantly boost performance. The best LLMs nearly match supervised systems but need extensive annotated data for demonstrations and high computational resources. All code and data are publicly released.
By Mohamed Adel, Bashar Alhafni, Nizar Habash
arXiv:2605. 02608v2 Announce Type: replace-cross Abstract: Transformer-based models achieve state-of-the-art dependency parsing for high-resource languages, yet their advantage over simpler architectures in low-resource settings remains poorly understood.
By Kevin Guan, Happy Buzaaba, Christiane Fellbaum
arXiv:2609.23955v1 Announce Type: new
Abstract: Previous work on Egyptian Arabic in NLP has focused largely on the prestigious Cairene Egyptian Arabic (CEA) dialect, resulting in a lack of representa...
By Mai Mohamed Eida, Ryan Dolan, Paul de Nijs, Jonathan Dunn
The paper reports Alkhder’s submission to Subtask 1 of the AlexandriaX-2026 shared task, translating English dialogue turns into thirteen Arabic dialects. The team fine‑tuned NileChat‑3B‑Base with QLoRA on the Alexandria parallel dialogue corpus, training a single set of adapter weights for all dialects. Each translation is conditioned on target country, dialect, domain, participant roles, speaker‑addressee gender direction, and preceding dialogue context, and the model is run autoregressively using its own previous outputs as context. In the constrained track, training takes about one hour and inference on the private test set takes under two hours, achieving a country‑macro‑averaged spBLEU of 20.36 and chrF++ of 36.64 on both tracks.
By Hasan Alkhder, Maher Pasa, Maria Hanini, Amro Najjar, Ahmet Zengin
Previous work on Egyptian Arabic in NLP has focused largely on the prestigious Cairene Egyptian Arabic (CEA) dialect, resulting in a lack of representation for the less prestigious Sa'idi Egyptian Ara...
arXiv:2606.13218v2 Announce Type: replace
Abstract: Arabic and Hebrew, as closely related Semitic languages, share many words with similar surface forms, including true cognates, false friends, and m...
By Junhong Liang, Noor Abo Mokh, Bashar Alhafni
arXiv:2609.22796v1 Announce Type: new
Abstract: Dialectal Arabic machine translation (MT) remains challenging despite recent progress in Arabic language technologies, particularly because effective t...
By Abdellah El Mekki, AbdelRahim A. Elmadany, Samar M. Magdy, Saad Ezzini, Mo El-Haj, Mustafa Jarrar, Zaid Alyafeai, Bernard Ghanem, Muhammad Abdul-Mageed
arXiv:2609.17539v1 Announce Type: new
Abstract: We present MudawanSn, a gold-standard resource of 1,271 sentence-aligned pairs manually translated from Wolof into Modern Standard Arabic (MSA). The so...
By Mouhamed Mbaye, Thierno Diop
Nuha‑Speech is a new initiative aimed at creating general‑purpose Arabic speech‑large language models (speech‑LLMs). It includes the construction of a large Arabic Speech Question‑Answering corpus with over 1.5 million samples for instruction tuning, supervised fine‑tuning of Qwen‑Omni model variants at various scales, and a systematic evaluation framework with diverse tasks and tailored metrics. The project seeks to establish foundational infrastructure for Arabic speech‑LLMs amid limited Arabic speech resources.
By Yingzhi Wang, Reem Alhazzani, Muhammad Alqurishi
EDRAC is the first large‑scale benchmark for dialectal Arabic machine reading comprehension and generative question answering, covering five major dialects—Egyptian, Moroccan, Emirati, Syrian, and Saudi. It contains 499 passages from naturally spoken interactions and 4,977 QA pairs produced via a human–LLM collaborative pipeline. The benchmark evaluates Arabic‑centric and multilingual large language models, revealing gaps between semantic answer quality and dialectal fidelity and underscoring limitations of current evaluation metrics for dialectal Arabic generation.
By Noor Abo Mokh, Kirill Chirkunov, Teresa Lynn, Nizar Habash, Reham Marzouk, Malik H. Altakrori, Younes Samih, Muhammed Abu Odeh, Nour Rabih, Rahaf Alshahrani, Hamad Alshehhi, Hamdan Al-Ali, Muhra Almahri, Besher Hassan, Mohamed Anwar, Abed Alhakim Freihat, Preslav Nakov, Alham Fikri Aji