---
title: "PolarizedTeam at SemEval-2026 Task 9: Detecting Multilingual,"
description: "The paper describes PolarizedTeam's entry to SemEval-2026 Task 9 (POLAR), which asks systems to… 11 references, cited by 1 paper here."
authors: ["Maria Nestor", "Maroan Al Shrafat", "Ioana Pește", "Daniela Gifu", "Diana Trandabăț"]
year: 2026
venue: "Proceedings of the 20th International Workshop on Semantic Evaluation (2026)"
source: "https://aclanthology.org/2026.semeval-1.180/"
doi: "10.18653/v1/2026.semeval-1.180"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/nestor-2026-polarizedteam-semeval-2026-task-9/"
---
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# PolarizedTeam at SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization

**Authors:** Maria Nestor, Maroan Al Shrafat, Ioana Pește, [Daniela Gifu](https://md.research.elamur.ai/free/en/authors/daniela-gifu/) and [Diana Trandabăț](https://md.research.elamur.ai/free/en/authors/diana-trandabat/) · **Year:** 2026 · **Venue:** [Proceedings of the 20th International Workshop on Semantic Evaluation (2026)](https://md.research.elamur.ai/free/en/venues/semeval/) ([SemEval 2026](https://md.research.elamur.ai/free/en/venues/semeval/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.semeval-1.180/) · [PDF](https://aclanthology.org/2026.semeval-1.180.pdf) · [DOI 10.18653/v1/2026.semeval-1.180](https://doi.org/10.18653/v1/2026.semeval-1.180) · [OpenAlex](https://openalex.org/W7166885275)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · [1](https://md.research.elamur.ai/free/en/papers/nestor-2026-polarizedteam-semeval-2026-task-9/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary

The paper describes PolarizedTeam's entry to SemEval-2026 Task 9 (POLAR), which asks systems to find polarized online content in 22 languages. Subtask 1 is a binary polarized/non-polarized decision. Subtask 2 assigns any of five polarization types: political, racial/ethnic, religious, gender/sexual and other. The texts are short, noisy and unevenly labelled, and some languages have almost no positives for certain categories.

The authors trained a separate model for each language. For Subtask 1 they compared mBERT, XLM-RoBERTa-Large and a hybrid that feeds mBERT token embeddings into a unidirectional LSTM. The hybrid scored best, with an average F1 of 0.76. For Subtask 2 they built up their system in three steps:

- an mBERT baseline using mean pooling and class-weighted BCE;
- XLM-RoBERTa-Base combining [CLS] and mean-pooled vectors, trained with Focal Loss;
- a final model in which every label is handled by a dedicated attention head paired with a separate classifier.

Macro-F1 rose with each step, reaching 0.507 for the final model. The work shows that label-aware modelling and imbalance handling help with rare categories and low-resource languages. Very short texts and the lack of cross-lingual transfer remain weak points.

## Key points

- **Data:** The POLAR dataset covers 22 languages, including Amharic, Hausa, Khmer, Odia, Russian and Polish, with roughly 1,700–7,000 snippets per language. For Subtask 2, all-zero (non-polarized) examples were downsampled to half the number of polarized ones.
- **Subtask 1 averages (F1 / accuracy):**
  - mBERT: 0.61 / 0.70
  - XLM-RoBERTa-Large: 0.67 / 0.71
  - mBERT+LSTM: 0.76 / 0.80
  - The best per-language hybrid scores were on Chinese (0.897) and Nepali (0.880).
- **Subtask 2 average macro-F1:**
  - mBERT baseline: 0.453
  - XLM-RoBERTa-Base with Focal Loss: 0.495
  - Label-wise attention: 0.507
  - The final model's gains were noted for skewed languages such as Amharic, Hausa and Telugu.
- **Training setup:** PyTorch with HuggingFace Transformers; AdamW at a learning rate of 1e-5 with linear decay; batch size 32–64; at most 5 epochs with early stopping; maximum length of 256 tokens; no lemmatization.
- **Code:** The code is released at github.com/FreaksMind/POLAR-SEMEVAL. The authors list remaining limitations as rare labels, very short texts, and per-language training that blocks cross-lingual transfer.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Alexandru Ioan Cuza University](https://md.research.elamur.ai/free/en/universities/alexandru-ioan-cuza-university/) — University, Iași, Romania · 8 works · 402 citations
- [Romanian Academy](https://md.research.elamur.ai/free/en/labs/romanian-academy/) — Research institute, Bucharest, Romania · 8 works · 841 citations
- [Academia Română, Filiala Iași](https://md.research.elamur.ai/free/en/labs/academia-romana-filiala-iasi/) — Research institute, Iași, Romania · 3 works · 3 citations
- [Academia Oamenilor de Știință din România](https://md.research.elamur.ai/free/en/labs/academia-oamenilor-de-stiinta-din-romania/) — Research institute, Bucharest, Romania · 3 works · 1 citation

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🧭 Topics (1): [🌍 Multilingual & cross-lingual NLP](https://md.research.elamur.ai/free/en/topics/multilingual-nlp/) · 828 works

## Cite

Cite as: Maria Nestor, Maroan Al Shrafat, Ioana Pește, Daniela Gifu and Diana Trandabăț. 2026. PolarizedTeam at SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization. Proceedings of the 20th International Workshop on Semantic Evaluation (2026).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{nestor2026polarizedteam,
  title = {{PolarizedTeam at SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization}},
  author = {Maria Nestor, Maroan {Al Shrafat}, Ioana Pește, Daniela Gifu and Diana Trandabăț},
  booktitle = {Proceedings of the 20th International Workshop on Semantic Evaluation (2026)},
  year = {2026},
  doi = {10.18653/v1/2026.semeval-1.180},
  url = {https://doi.org/10.18653/v1/2026.semeval-1.180}
}
```

- [← previous PolAR Bears at SemEval-2026 Task 9: Parameter-Efficient Fine-Tuning and Cross-Lingual Augmentation for Multilingual Polarization Detection](https://md.research.elamur.ai/free/en/papers/ulli-2026-polar-bears-semeval-2026-task/)
- [next → Polynomial Expansion Rank Adaptation: Enhancing Low-Rank Fine-Tuning with High-Order Interactions](https://md.research.elamur.ai/free/en/papers/zhang-2026-polynomial-expansion-rank-adaptation-enhancing/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/16/)

## References

11 references, 3 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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6. Lo, Soda Marem and Stranisci, Marco A. and Cignarella, Alessandra Teresa and Frenda, Simona and Basile, Valerio and Bosco, Cristina and Jezek, Elisabetta and Patti, Viviana. 2025. Subjectivity in stereotypes against migrants in Italian : an experimental annotation procedure. In Proceedings of the El… [find](https://scholar.google.com/scholar?q=Lo%2C+Soda+Marem+and+Stranisci%2C+Marco+A%2E+and+Cignarella%2C+Alessandra+Teresa+and+Frenda%2C+Simona+and+Basile%2C+Valerio+and+Bosco%2C+Cristina+and+Jezek%2C+Elisabetta+and+Patti%2C+Viviana%2E+2025%2E+Subjectivity+in+ster)
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*Updated 31 October 2026*