@inproceedings{mhaouach-etal-2026-bdsi,
title = "{BDSI} at {A}ra{S}ent{E}val Shared Task : A Multi-Transformer Contrastive Learning for {A}rabic Dialect Sentiment Analysis",
author = "M{'}haouach, Mohamed and
Elyoussoufi, Kaouthar and
Benlahbib, Abdessamad and
Alami, Hamza",
editor = "Al-Khalifa, Hend and
El-Haj, Mo and
Ezzini, Saad",
booktitle = "The 7th Workshop on Open-Source {A}rabic Corpora and Processing Tools ({OSACT}7) with 5 Shared Tasks",
month = may,
year = "2026",
address = "Palma, Mallorca (Spain)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.osact-1.39/",
doi = "10.63317/4prectoefpgj",
pages = "284--287",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T BDSI at AraSentEval Shared Task : A Multi-Transformer Contrastive Learning for Arabic Dialect Sentiment Analysis
%A M’haouach, Mohamed
%A Elyoussoufi, Kaouthar
%A Benlahbib, Abdessamad
%A Alami, Hamza
%Y Al-Khalifa, Hend
%Y El-Haj, Mo
%Y Ezzini, Saad
%S The 7th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT7) with 5 Shared Tasks
%D 2026
%8 May
%I Association for Computational Linguistics
%C Palma, Mallorca (Spain)
%F mhaouach-etal-2026-bdsi
%X 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.
%R 10.63317/4prectoefpgj
%U https://aclanthology.org/2026.osact-1.39/
%U https://doi.org/10.63317/4prectoefpgj
%P 284-287
Markdown (Informal)
[BDSI at AraSentEval Shared Task : A Multi-Transformer Contrastive Learning for Arabic Dialect Sentiment Analysis](https://aclanthology.org/2026.osact-1.39/) (M’haouach et al., OSACT 2026)
ACL