Stance detection continues to be an important task sitting at the intersection of Natural Language Processing (NLP) and Computational Social Science (CSS). In this work, we evaluate how different variations of BERT models perform on the cross-topic form of the task. In particular, we inspect their performance on the second subtask of the shared task StanceNakba 2026, where two topics are included, namely Arab Normalization with Israel and The Presence of Refugees in Arab Countries. We find that the best-performing model was bert-base-arabertv02-twitter, and we further improve its performance by providing context about the topic during the training phase, achieving an F1-score of 0.86 and ranking second among the participating teams.

We describe Ṣaqr, ranked first on both scored tracks of the AISA-ArabicFC shared task on Arabic function calling, scoring 0.9279 on Track A and 0.9400 on Track B over a 1,125- instance blind test set. The system is deliber- ately plain: one Qwen3-4B model with LoRA, two epochs, provided data only. Our contri- bution is not the system but an accounting of where score comes from. We find that scale helped only up to a point in our single-run, cross-family comparison, and that the remain- ing gains came instead from inference-time de- cisions worth +0.0063 Overall A and +0.0223 Overall B (Appendix A) with no retraining. prefilling the reasoning trace so the model can- not skip it, and parsing arguments against the declared schema . We also report two proper- ties of the data that bound any system’s score: abstention is decidable from prompt structure without reading the query, and the gold anno- tation is self-inconsistent on precisely the slots that separate strong systems.

This paper presents a novel approach to Ara-bic Word Sense Disambiguation (WSD) lever-aging transformer-based models to tackle thecomplexities of the Arabic language. Utiliz-ing the SALMA dataset, we applied severaltechniques, including Sentence Transformerswith Siamese networks and the SetFit frame-work optimized for few-shot learning. Our ex-periments, structured around a robust evalua-tion framework, achieved a promising F1-scoreof up to 71%, securing second place in theArabicNLU 2024: The First Arabic NaturalLanguage Understanding Shared Task compe-tition. These results demonstrate the efficacyof our approach, especially in dealing with thechallenges posed by homophones, homographs,and the lack of diacritics in Arabic texts. Theproposed methods significantly outperformedtraditional WSD techniques, highlighting theirpotential to enhance the accuracy of Arabicnatural language processing applications.