BDSI at AraSentEval Shared Task : A Multi-Transformer Contrastive Learning for Arabic Dialect Sentiment Analysis
Year: 2026
Type: conference-paper
Abstract: This paper presents our system for the AraSentEval 2026 shared task on Arabic dialect sentiment analysis.We propose a multi-model ensemble combining AraBERTv2 and CAMeLBERT with supervised contrastive learning to improve sentiment classification.The system incorporates dialect-aware preprocessing, class-weighted cross-entropy loss with label smoothing, supervised contrastive loss for enhanced sentence representations, and rule-based post-processing for dialect-specific patterns.Our approach achieves a macro F1-score of 0.83 on the official test set, demonstrating the effectiveness of contrastive learning with pretrained Arabic language models for dialectal sentiment analysis.
Institution: Sidi Mohamed Ben Abdellah University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166594916