Request Correction
Use this form to request corrections to the paper metadata. Select the fields that need correction and provide the correct information.
Correction Guidelines
- Click the edit button next to a field to report a correction.
- Fill in the suggested correction value for each field you want to correct.
- Provide your name and email so we can contact you if needed.
Paper Information
A2NLP at StanceNakba Shared Task: Fine-Tuned AraBERT for Topic-Based Arabic Stance Detection
Paper Fields
Click the edit button next to a field to report a correction.
A2NLP at StanceNakba Shared Task: Fine-Tuned AraBERT for Topic-Based Arabic Stance Detection
AbstractThis paper describes A2NLP’s system for Subtask B of the StanceNakba Shared Task, which addresses cross-topic Arabic stance detection. The goal is to classify sentence–topic pairs into pro, against, or neutral labels. We introduce a topic-conditioned prompting strategy built on AraBERTv0.2-Twitter, where each instance is reformulated into a structured prompt that explicitly models the interaction between the sentence and its target topic. The model is trained using 5-fold stratified cross-validation with class-weighted loss to ensure robustness under mild label imbalance. Our final submission achieves a Macro-F1 score of 0.8483 on the official test set, outperforming the AraBERTv2 baseline (0.810) and ranking fifth overall. Ablation analysis confirms that topic-conditioned prompting substantially improves generalization across topics. The findings demonstrate the importance of structured input design and domain-aligned pretraining for reliable stance detection in dialectal Arabic social media discourse.
Authors
Expand an author to correct their information. Use the remove button to request author removal, or add a new author.
PDF Attachment
You may attach a PDF as a corrected version of the paper. Max file size: 10MB. Only PDF files are accepted.
Your Information
Author Declaration *
Select at least one field to correct using the edit buttons above.