This paper presents the first investigation into the performance of the Conformer architecture for hierarchical text classification, specifically applied to predicting two-level Arabic text genres. The proposed system combines multilingual sentence embeddings, target label definition representations, a Conformer projection module, an auxiliary broad-genre classifier, synthetic data augmentation, and seed-based ensembling. Evaluated on the official shared task, the approach achieved a Hierarchical Macro-F1 of 0.4399, demonstrating strong high-level genre classification (Broad Macro-F1 of 0.6288) while highlighting ongoing challenges in distinguishing semantically close specific subcategories (Specific Macro-F1 of 0.2511).

We present our system for Daleel 2026 Task 2 on Arabic Argumentative Discourse Unit (ADU) detection. Our main contribution is a Conformer-based tagging architecture that combines a pretrained AraBERT encoder with a Conformer head to capture both global discourse context and local span-boundary cues. To the best of our knowledge, this is the first application of a Conformer-based architecture to Arabic ADU detection. The system also uses overlapping token windows, class-weighted training, character-level span reconstruction, and lightweight post-processing. On the official hidden test set, our system achieved an overall F1 score of 0.732, substantially outperforming the official baseline of 0.285 and ranking fourth in the closed track.