MKJ at SemEval-2026 Task 9: A Comparative Study of Generalist, Specialist, and Ensemble Strategies for Multilingual Polarization
Apr 13, 2026·
Maziar Kianimoghadam Jouneghani
University of Turin
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0 min readAbstract
We present a systematic study of multilingual polarization detection across 22 languages for SemEval-2026 Task 9 (Subtask 1), contrasting multilingual generalists with language-specific specialists and hybrid ensembles. While a standard generalist like XLM-RoBERTa suffices when its tokenizer aligns with the target text, it may struggle with distinct scripts (e.g., Khmer, Odia) where monolingual specialists yield significant gains. Rather than enforcing a single universal architecture, we adopt a language-adaptive selection strategy that chooses among multilingual generalists, language-specific specialists, and hybrid ensembles based on development performance. Additionally, cross-lingual augmentation via NLLB-200 yielded mixed results, often underperforming native architecture selection and degrading morphologically rich tracks. Our final system achieves an overall macro-averaged F1 score of 0.796 and an average accuracy of 0.826 across all 22 tracks. Code and final test predictions are publicly available at: https://github.com/Maziarkiani/SemEval2026-Task9-Subtask1-Polarization.
Type
Publication
Proceedings of the 20th International Workshop on Semantic Evaluation (SemEval-2026)
Authors
NLP Researcher
I’m an NLP and Computational Linguistics researcher with a Master’s degree in Language Technologies and Digital Humanities from the University of Turin, where I graduated in July 2026. I’m interested in the intersection of language, culture, and AI.
My master’s thesis, supervised by Prof. Viviana Patti and Dr. Marco Antonio Stranisci, grew out of a community-developed dataset on information disorder (InDor). It proposed a retrieval-based framework to connect LLMs’ reasoning to native-speaker explanations in order to better assess information disorder, and tested it on Persian and Italian.
My Bachelor’s degree is in English language translation, focused on linguistics and translation studies.
Also, beyond academia, I spent over 3 years in SEO, web content, and digital marketing, work that showed me how language functions for real audiences, how it circulates online and how we can use it for marketing and business goals.
My research interests are: Multilingual & Cross-Cultural NLP, Computational Social Science (CSS), Explainable AI, In-Context Learning, Human-in-the-Loop AI, and Information Disorder.
My master’s thesis, supervised by Prof. Viviana Patti and Dr. Marco Antonio Stranisci, grew out of a community-developed dataset on information disorder (InDor). It proposed a retrieval-based framework to connect LLMs’ reasoning to native-speaker explanations in order to better assess information disorder, and tested it on Persian and Italian.
My Bachelor’s degree is in English language translation, focused on linguistics and translation studies.
Also, beyond academia, I spent over 3 years in SEO, web content, and digital marketing, work that showed me how language functions for real audiences, how it circulates online and how we can use it for marketing and business goals.
My research interests are: Multilingual & Cross-Cultural NLP, Computational Social Science (CSS), Explainable AI, In-Context Learning, Human-in-the-Loop AI, and Information Disorder.