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arXiv Computation and Language By Ahmed M. Fetouh, Mariam Labib, Nsrin Ashraf, Omer Dawood, Hamada Nayel

REGLAT at ArGuard Shared Tasks: An Ensemble-Based Model Integrating NER for Harmful Content Detection in Arabic

Read the original on arXiv Computation and Language →

The paper introduces REGLAT, an ensemble-based model that integrates named entity recognition (GLiNER) with a fine‑tuned MARBERT classifier to detect harmful content in Arabic. It classifies user prompts into seven harm categories, achieving a macro‑F1 of 0.886 and accuracy of 0.897 on the ArGuard 2026 development set, with slightly lower scores on blind test sets. The study highlights that combining explicit entity extraction with contextual language modeling improves fine‑grained harmful content detection, though distinguishing closely related categories remains difficult.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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