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
Comparative Study of Machine Learning and Transformer-Based Approaches for Arabic Politeness Detection at AdabEval 2026
Paper Fields
Click the edit button next to a field to report a correction.
Comparative Study of Machine Learning and Transformer-Based Approaches for Arabic Politeness Detection at AdabEval 2026
This paper describes our system submitted to the OSACT7 AdabEval shared task on Arabic politeness detection (TaskA). The task requires classifying Arabic texts into three categories: Polite, Impolite, and Neutral. We systematically explore multiple approaches, progressing from classical machine learning baselines using pre-trained embeddings to fine-tuned transformer models. Our best system leverages MARBERT, a transformer model pre-trained on one billion Arabic tweets, fine-tuned with Focal Loss to handle the significant class imbalance present in the dataset (70% Neutral). We additionally experiment with hybrid approaches combining fine-tuned embeddings with gradient-boosted classifiers and ensemble methods. Our best single model achieves a macro F1 score of 0.84 and an accuracy of 0.90 on the validation set, substantially outperforming classical ML baselines (F1 = 0.42).
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.