The paper introduces MADARTB, the first large-scale parallel Arabic dependency treebank covering five city dialects—Beirut, Cairo, Doha, Rabat, and Tunis—alongside Modern Standard Arabic. It contains 5,000 sentences annotated with the CATiB dependency scheme and shows that while overall construction profiles are similar across varieties, local syntactic structures often diverge, indicating significant structural variation. A supervised parser achieves 87.5% LAS, outperforming the best LLM setup at 85.7% LAS across all six varieties, and the authors release the annotations and models publicly.
By Noor Abo Mokh, Mohamed Adel, Jamila El-Gizuli, Go Inoue, Ahmed Elshabrawy, Nizar Habash
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
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
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
arXiv:2609.10153v1 Announce Type: new
Abstract: Arabic morphology remains challenging for large language models, since fluent generation does not guarantee accurate morphosyntactic control. Existing...
By Mahmoud Reda, Salam Khalifa, Reham Marzouk, Nizar Habash
The paper introduces STAR‑Ar, a BERT‑BiLSTM‑CRF model designed for the Daleel 2026 Arabic argument mining shared task. It treats argument discourse unit detection and classification as a token‑level sequence labeling problem, achieving an F1‑score of 72.69 on validation and 73.7 on test data. Analysis shows that models trained only on editorial texts perform worse than those trained on debates, mainly due to the smaller editorial dataset.
By Bhuvanesh Verma, Ali Abusaleh, Alexander Mehler