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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.
This study evaluates large language models (LLMs) for English‑to‑dialectal Arabic machine translation using the MADAR dataset across 16 Arabic dialects, covering both translation directions. It compares specialized Arabic models (Jais, Nile), multilingual models (Gemma, Command‑R, Mistral, Aya), and commercial APIs (GPT‑4.1) with multiple metrics—BLEU, CHRF, COMET, GEMBA—and a small manual assessment. The authors also examine LLMs’ ability to classify dialects and discuss challenges in automatic MT evaluation for Arabic dialects, noting potential training data contamination.
arXiv:2011.03783v3 Announce Type: replace-cross Abstract: In this work, we introduce the construction of a machine translation (MT) assisted and human-in-the-loop multilingual parallel corpus with an...
NADI 2026 is the seventh edition of the Nuanced Arabic Dialect Identification shared task series and the second focused on multidialectal Arabic speech processing. It includes five main tasks—Automatic Speech Recognition, Spoken Dialect Identification, Text-to-Speech, Spoken Language Translation, and Spoken Language Understanding—along with eight subtasks that test realistic scenarios such as low‑bandwidth, mixed dialects, code‑switching, out‑of‑domain, and zero‑shot settings. The event attracted 21 teams from at least 13 countries, with 48 test‑phase submissions and 14 system‑description papers, and the results highlight out‑of‑domain generalization as a major bottleneck while showcasing the strengths of Arabic‑specialized speech models, multimodal dialect identification, and ensemble methods.
arXiv:2611.06517v1 Announce Type: new Abstract: The massive permeability of intelligent systems in every sphere of human activities have greatly enhanced clarity, ease of work, and increased general...
arXiv:2609.10395v1 Announce Type: new Abstract: This paper describes the Rosetta system for Subtask 1 (Context-Aware English-to-Dialectal Arabic Dialogue Translation) of the AlexandriaX shared task,...