This paper presents the system developed by the NU-SentEval team for Subtask 1 (Sentiment Analysis in Dialectal Arabic) of the DialectSentEval 2026 Shared Task. Arabic dialect sentiment classification is challenging due to morphological complexity, orthographic variation across regional dialects, and class imbalances in user reviews. To address these challenges, we propose an ensembled architecture leveraging five diverse Pre-trained Language Models (PLMs): MARBERTv2, MARBERT, CAMeLBERT-DA, AraBERTv0.2-Twitter, and Twitter-XLM-RoBERTa. All models were fine-tuned using a customized Focal Loss objective combined with Label Smoothing to mitigate majority class bias and prevent overconfidence. Out-of-fold probability representations from individual models were aggregated through a Logistic Regression stacking meta-classifier (stacking_c1). On the official test set, our system achieved a Macro F1-score of 0.8141 and an Accuracy of 0.8343, demonstrating the effectiveness of combining specialized dialectal representations with loss regularization and meta-learning.

We present NU-SentEval’s system for DialectSentEval 2026 Subtask 2 (Arabic Dialect Sentiment Swap), which rewrites Arabic dialectal sentences into the opposite sentiment polarity while preserving their core content. Our approach fine-tunes AraT5v2-base-1024, a pretrained Arabic sequence-to-sequence model, directly on the shared task’s source-target sentence pairs, and generates outputs using standard beam search decoding. On the official Final Evaluation test leaderboard, our system achieves 54.63 BLEU, 72.61 chrF, and 68.98% sentiment preservation. Our manual error analysis shows the model reliably inverts sentiment for short, single-clause inputs, often matching the human reference exactly, though it occasionally fails to propagate the sentiment flip to secondary cues such as emojis, resulting in mixed-signal outputs.

We present our closed-track systems for both subtasks of Daleel 2026, which targets Arabic argumentative discourse unit (ADU) mining over editorials and competitive debates. Task 1 is an imbalanced multi label paragraph classification problem, and Task 2 requires character-level detection and classification of ADU spans. For Task 1 we investigate two parameter-efficient decoder-LLM formulations: joint label generation with Qwen3.5-9B and independent label scoring with ALLaM-7B. The latter decomposes each paragraph into six explicit Yes/No decisions, giving every label direct supervision and a separate decision score; it achieves 0.6714 Macro-F1 on the evaluation phase and ranked 4th, while ablations show that additional class-balanced weighting degrades performance. For Task 2 we combine task-adaptive pretraining, Beginning-Inside-Outside Conditional Random Field (BIO-CRF) sequence labeling, boundary-jitter augmentation, character-space ensembling, and span post-processing over AraBERT-large, achieving an official F1 of 0.7523 (0.7931 on debates, 0.6417 on editorials) and ranking second. Across both tasks, our results support explicit label-wise supervision and span-level ensemble decisions as effective ways to handle label imbalance and boundary noise without aggressive reweighting, while short spans, rare classes, and genre variation remain challenging.