Sentence segmentation in Arabic is challenging due to ambiguous and inconsistent punctuation, with many texts lacking reliable sentence boundary markers. Existing approaches rely heavily on punctuation cues and are typically evaluated on well-formed text, limiting their robustness in realistic Arabic settings. To address this, we introduce AraSEG, a genre-diverse sentence segmentation corpus spanning eight genres and a wide range of punctuation and document structure conditions. Using AraSEG, we evaluate LLMs, lightweight encoder models, and dependency parser-based models under increasingly challenging segmentation settings. Our experiments show that lightweight encoders, and even dependency parser-based models, outperform LLMs under the hardest conditions. We further investigate the effects of training data size and genre diversity, finding that performance eventually saturates and cross-genre generalization remains challenging. We also demonstrate that accurate sentence segmentation substantially improves downstream dependency parsing. We make our code, data, and models publicly available.

We present the findings of the Arabic Sentence Segmentation (AraSEG) Shared Task, organized at the Fourth Arabic Natural Language Processing Conference (ArabicNLP 2026). Given an Arabic document, the task requires systems to identify sentence boundaries and segment the text into coherent sentences. Systems are evaluated on the genre-diverse AraSEG corpus under four input settings that vary in punctuation availability and paragraph structure, each with a Closed Track restricted to the provided training data and an Open Track permitting publicly available external resources. 22 teams from 13 countries registered, 16 submitted systems, and 14 submitted system-description papers. Across the eight subtask-track combinations, the best systems achieve F1 scores ranging from 89.1 to 95.3. Performance declines as segmentation cues are removed, with the largest drop occurring when both punctuation and paragraph structure are unavailable. Pretrained-encoder ensembles and careful prediction calibration emerge as the most effective approaches, while gains from LLMs, external data, and complex architectures vary across systems and settings.

We present an innovative and efficient modeling framework for cross-lingual named entity recognition (NER), leveraging the strengths of knowledge distillation and consistency training. Our approach distills knowledge from an XLM-RoBERTa model pre-trained on a high-resource source language (English) to a student model, which then undergoes semi-supervised consistency training with KL divergence loss on a low-resource target language (Arabic). We focus our application on the financial domain, using a small, sourced dataset of financial transactions as seen in SMS messages Using datasets comprising SMS messages in English and Arabic containing financial transaction information, we aim to transfer NER capabilities from English to Arabic with minimal labeled Arabic samples. The framework generalizes named entity recognition from English to Arabic, achieving F1 scores of 0.74 on the Arabic financial transaction dataset and 0.61 on the WikiANN dataset, surpassing or closely competing with models that have 1.7 and 5.3 more parameters, respectively, while efficiently training it on a single T4 GPU. Our experiments show that using a small number of labeled data for low-resource cross-lingual NER applications is a wiser choice than utilizing zero-shot techniques while also using up fewer resources. This framework holds significant potential for developing multilingual applications, particularly in regions where digital interactions span English and low-resource languages.