We describe Soloeval’s submission to the Sentence-Level Open Track of the BAREC Shared Task 2026 on fine-grained Arabic readability assessment. Rather than treating the 19-level readability scale as a scalar regression target, we fine-tune pretrained Arabic BERT encoders (MARBERTv2 and CAMeLBERT-Mix) with a soft-label cross-entropy objective whose target distribution is a Gaussian centered on the gold level, chosen to more directly reflect the Quadratic Weighted Kappa (QWK) evaluation metric’s quadratic distance penalty. We combine this with lightweight auxiliary surface features (sentence length, word count, diacritic density) and K-fold cross-validation. Our best single-model configuration, CAMeLBERT-Mix trained with F=3 folds, achieves a QWK of 80.6 on the BAREC 2026 blind test leaderboard for the Open track. We report our full hyperparameter search, discuss the gap between our dev- and test-set results, and outline what we would try next.

We describe our submission to Task 1 (Argumentative Discourse Unit Classification) of the Daleel 2026 shared task on Arabic argument mining, in the closed track. Given a paragraph from an Arabic editorial or debate transcript, the task is to predict the multi-label set of argumentative functions it contains, drawn from six classes: common ground, assumption, testimony, statistics, anecdote, and other. Our system fine-tunes a pretrained Arabic encoder, comparing AraBERT, MARBERT, and CAMeLBERT-mix, augmented with a small side-channel of hand-crafted lexical features motivated directly by the task’s class definitions (e.g., reporting verbs for testimony, digit/percentage patterns for statistics), trained with a class-balanced focal loss and combined into a 5-fold cross-validation ensemble; CAMeLBERT-mix was selected as the final encoder for all submitted configurations. In the dev phase, our Both-domain configuration achieved an overall macro-F1 of 0.5600 (editorial 0.4350, debate 0.4900). On the official test-phase Codabench leaderboard, our Both-domain configuration achieved an overall macro-F1 of 0.5265 (editorial 0.3838, debate 0.4943), ranking 10th; our Editorial-only configuration reached an overall macro-F1 of 0.4544 (editorial 0.3249, debate 0.4509). We report two findings grounded in these results: overall (pooled) macro-F1 consistently exceeds the macro-F1 computed on either genre subset alone, and our editorial-only model unexpectedly scores higher macro-F1 on the out-of-domain debate subset than on its own in-domain editorial subset. We discuss both as likely artifacts of per-class support sparsity at the smaller genre-subset scale rather than genuine cross-domain superiority, though we did not conduct a formal ablation study or systematic error analysis to confirm this, which we flag as the primary limitation of this report. Our code is available at https://github.com/turab-usmani/daleel-task1-closedtrack.