Cross-domain Arabic Named Entity Recognition (NER) remains challenging because models trained on one domain can lose performance when applied to unseen domains with different vocabulary, writing styles, and entity distributions. This paper addresses this challenge, and presents Yafa’s Team submission (ranked 7^th) to Subtask 1 of the KnowledgeGraphEval 2026 Shared Task. Our system leverages an AraBERTv2-based model for nested Arabic NER to approach it. The proposed approach combines task-adaptive (TAPT) pretraining on unlabeled Konooz data, R-Drop fine-tuning on Wojood, strong data augmentation, teacher-student knowledge distillation, span-level confidence and entropy filtering, and an entity-specific confidence-aware weighted-logit ensemble. Three teacher models generate soft labels for Konooz, and three distilled student models are combined in the final ensemble to produce the final predictions. Wojood contains approximately 448K tokens and 72K entity mentions across 21 entity types, while Konooz contains data from 10 domains for cross-domain evaluation. The final system achieves a Micro F1 of 71.33% on the official Konooz test set, improving over the non-adapted AraBERTv02 baseline of 61.00% by 10.33 points. The results show that combining domain adaptation, filtered knowledge transfer, and entity-specific ensembling can improve cross-domain Arabic NER performance.

Relation extraction (RE) identifies semantic relations between entities, converting unstructured text into structured facts for downstream applications such as knowledge graph construction. Arabic RE remains challenging due to rich morphology, complex syntax, dialectal variation, and the scarcity of high-quality annotated resources. We present Yafa’s Team submission (ranked 7th) to Subtask 2 of the KnowledgeGraphEval 2026 Shared Task, which frames Arabic RE as multiclass classification over 40 relation types from the dataset. We propose an ArBERTv2-based entity-marker classifier that fuses global sentence context ([CLS]) with entity-specific representations (h_e1, h_e2) into a joint representation for relation prediction. Building on this baseline, we exploit a transductive self-training step that pseudo-labels the released test inputs at a softmax confidence threshold of 0.95 and retrains on them, raising Micro-F1 from 70.20% to 91.92%. We further conduct a systematic analysis of cross-backbone ensembling with MARBERTv2 and AraBERTv2, finding that ensembling does not outperform the single transductive model. Our results highlight transductive self-training as a simple yet effective strategy for improving Arabic RE robustness.

In this paper, we present a high-performing model for Arabic stance detection on the STANCEEVAL2024 shared task part ofARABICNLP2024. Our model leverages ARABERTV1; a pre-trained Arabic language model, within a single-task learning framework. We fine-tuned the model on stance detection data for three specific topics: COVID19 vaccine, digital transformation, and women empowerment, extracted from the MAWQIF corpus. In terms of performance, our model achieves 73.30 macro-F1 score for women empowerment, 70.51 for digital transformation, and 64.55 for COVID-19 vaccine detection.