@inproceedings{mohamed-etal-2026-nileun,
    title = "{N}ile{U}n at {A}bjad{G}en{E}val Shared Task: Contrastive Learning with Stacking Ensemble for Efficient {A}rabic {AI}-Generated Text Detection",
    author = "Mohamed, Mohamed Hussein  and
      Shalaby, Shrouk  and
      Mohamed, Nesreen",
    booktitle = "Proceedings of the 2nd Workshop on {NLP} for Languages Using {A}rabic Script",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/manual-author-scripts/2026.abjadnlp-1.61/",
    pages = "489--492",
    abstract = "We present a computationally efficient ap- proach for detecting AI-generated Arabic text as part of the AbjadGenEval shared task. Our method combines Supervised Con- trastive Learning with a Stacking Ensemble of AraBERT and XLM-RoBERTa models. Our training pipeline progresses through three stages: (1) standard fine-tuning without con- trastive loss, (2) adding supervised contrastive loss for better embeddings, and (3) further fine-tuning on diverse generation styles. On our held-out test split, the stacking ensemble achieves F1=0.983 before fine-tuning. On the official workshop test data, our system achieved 4th place with F1=0.782, demonstrating strong generalization using only encoder-based trans- formers without requiring large language mod- els. Our implementation is publicly available"
}