@inproceedings{djamai-etal-2026-namaa,
title = "{NAMAA} at {S}em{E}val-2026 Task 9: Comparing Generative, Retrieval-Augmented, and Discriminative Methods for {A}rabic Online Polarization Detection and Type Classification",
author = "Djamai, Abdelbasset and
Al-Madi, Sahara and
Al-Zaid, Norah and
Al Jallad, Khloud and
Azim, Mona",
editor = "Kochmar, Ekaterina and
Ghosh, Debanjan and
North, Kai and
Komachi, Mamoru",
booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.semeval-1.439/",
pages = "3562--3590",
ISBN = "979-8-89176-414-9",
abstract = "Detecting polarization in online discourse is important for understanding social fragmentation , yet it remains difficult for Arabic due to dialect variation, informal writing, and implicit framing. In this paper, we study Arabic polarization modeling in the SemEval-2026 Task 9 (POLAR) setting, focusing on polarization detection (ST1) and polarization type classification (ST2). We compare three approaches: encoder fine-tuning, zero-shot prompting, and retrieval-augmented in-context learning (RAG-ICL), across six Arabic encoders and different LLMs. For ST1, RAG-ICL with Gemma-3-27b-it achieves the best result (test macro F1 = 0.83), while remaining competitive with the best fine-tuned encoder (0.82), and substantially outperforming zero-shot prompting. For ST2, a pipeline that first applies the best ST1 encoder as a hard filter and then performs RAG-ICL achieves a macro F1 = 0.62. Prompt-language effects are model-and task-dependent, with some settings doing better with English prompts and others with Arabic prompts. Chain-of-thought, self-refinement, and contrastive prompting do not outperform standard RAG-ICL."
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<abstract>Detecting polarization in online discourse is important for understanding social fragmentation , yet it remains difficult for Arabic due to dialect variation, informal writing, and implicit framing. In this paper, we study Arabic polarization modeling in the SemEval-2026 Task 9 (POLAR) setting, focusing on polarization detection (ST1) and polarization type classification (ST2). We compare three approaches: encoder fine-tuning, zero-shot prompting, and retrieval-augmented in-context learning (RAG-ICL), across six Arabic encoders and different LLMs. For ST1, RAG-ICL with Gemma-3-27b-it achieves the best result (test macro F1 = 0.83), while remaining competitive with the best fine-tuned encoder (0.82), and substantially outperforming zero-shot prompting. For ST2, a pipeline that first applies the best ST1 encoder as a hard filter and then performs RAG-ICL achieves a macro F1 = 0.62. Prompt-language effects are model-and task-dependent, with some settings doing better with English prompts and others with Arabic prompts. Chain-of-thought, self-refinement, and contrastive prompting do not outperform standard RAG-ICL.</abstract>
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%0 Conference Proceedings
%T NAMAA at SemEval-2026 Task 9: Comparing Generative, Retrieval-Augmented, and Discriminative Methods for Arabic Online Polarization Detection and Type Classification
%A Djamai, Abdelbasset
%A Al-Madi, Sahara
%A Al-Zaid, Norah
%A Al Jallad, Khloud
%A Azim, Mona
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F djamai-etal-2026-namaa
%X Detecting polarization in online discourse is important for understanding social fragmentation , yet it remains difficult for Arabic due to dialect variation, informal writing, and implicit framing. In this paper, we study Arabic polarization modeling in the SemEval-2026 Task 9 (POLAR) setting, focusing on polarization detection (ST1) and polarization type classification (ST2). We compare three approaches: encoder fine-tuning, zero-shot prompting, and retrieval-augmented in-context learning (RAG-ICL), across six Arabic encoders and different LLMs. For ST1, RAG-ICL with Gemma-3-27b-it achieves the best result (test macro F1 = 0.83), while remaining competitive with the best fine-tuned encoder (0.82), and substantially outperforming zero-shot prompting. For ST2, a pipeline that first applies the best ST1 encoder as a hard filter and then performs RAG-ICL achieves a macro F1 = 0.62. Prompt-language effects are model-and task-dependent, with some settings doing better with English prompts and others with Arabic prompts. Chain-of-thought, self-refinement, and contrastive prompting do not outperform standard RAG-ICL.
%U https://aclanthology.org/2026.semeval-1.439/
%P 3562-3590
Markdown (Informal)
[NAMAA at SemEval-2026 Task 9: Comparing Generative, Retrieval-Augmented, and Discriminative Methods for Arabic Online Polarization Detection and Type Classification](https://aclanthology.org/2026.semeval-1.439/) (Djamai et al., SemEval 2026)
ACL
- Abdelbasset Djamai, Sahara Al-Madi, Norah Al-Zaid, Khloud Al Jallad, and Mona Azim. 2026. NAMAA at SemEval-2026 Task 9: Comparing Generative, Retrieval-Augmented, and Discriminative Methods for Arabic Online Polarization Detection and Type Classification. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 3562–3590, San Diego, California, USA. Association for Computational Linguistics.