BDSI at AraSentEval Shared Task : A Multi-Transformer Contrastive Learning for Arabic Dialect Sentiment Analysis

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.

Resources

Details