This paper describes the system submitted by the phonghoang team for Track 1 (Seen Tar- gets) of the StanceEval-2026 shared task on Arabic stance detection, where models are trained on provided targets and evaluated on a related target within the same thematic do- main. We propose a two-stage framework to address this within-domain target shift. In the first stage, we fine-tune a Multi-Task MAR- BERT model that jointly learns stance, senti- ment, and sarcasm, further regularized with R- Drop and Stochastic Weight Averaging (SWA), achieving Favg2 of 0.8561 on the development set. In the second stage, we adapt to the test target (“Women Driving”) by combining con- trastive few-shot pseudo-labeling via a Large Language Model, fusion with an external Ara- bic stance dataset, and high-confidence samples filtered from a thematically related training tar- get. The resulting augmented training set is used to adapt our optimal Stage 1 architecture, achieving an official Favg2 of 0.8431 on the test set.

This paper presents the system submitted by team HP for the AraGenre 2026 Shared Task on Hierarchical Definition-Guided Arabic Genre Classification. To address the low-resource constraint of the provided dataset (140 labeled training samples), we propose a two-stage pipeline combining LLM-based pseudo-labeling with supervised fine-tuning. In the first stage, we evaluate several models under a 5-fold cross-validation framework, including Gemini 3.5 Flash Lite (0.9413), Qwen2.5 7B (0.7203), LLaMA 3.1 8B (0.6380), and a multilingual NLI model (0.3099). Based on these results, Gemini 3.5 Flash Lite is selected to generate pseudo-labels for the unlabeled test set using a definition-guided few-shot prompt. In the second stage, MARBERTv2 and ARBERTv2 are fine-tuned on approximately 28,000 pseudo-labeled samples. Our experiments show that MARBERTv2 achieves the best standalone score of 0.7169 Hierarchical Macro F1, outperforming both ARBERTv2 (0.7118) and a soft voting ensemble (0.7165), suggesting that Arabic-specialized pretraining of MARBERTv2 is more effective than model ensembling for this task.

This paper describes the system submitted by team HP for the BAREC Shared Task 2026 on Arabic readability assessment. Driven by the Quadratic Weighted Kappa (QWK) eval- uation metric, which heavily penalizes dis- tant misclassifications, we reframe the 19- level task as an ordinal regression problem rather than standard multi-class classification. Our approach leverages pre-trained language models (AraBERTv02, ARBERT) optimized via Parameter-Efficient Fine-Tuning (LoRA) and trained with a COnsistent RAnk Log- its (CORAL) loss framework incorporating sample-level class weights. To ensure robust generalization, we consolidate the available datasets and implement a 5-fold stratified cross- validation pipeline. Finally, to optimize the QWK metric on the official blind test, we apply an 18-threshold Nelder-Mead optimizer cali- brated exclusively on blended Out-Of-Fold log- its, yielding a blind test QWK of 0.837. Our solution is released in the Github.