---
title: "Taien at SemEval-2026 Task 9: Multilingual Polarization"
description: "The authors tackle Subtask 1 of SemEval-2026 Task 9. 21 references, cited by 1 paper here."
authors: ["Saida Taien", "Palash Hossen"]
year: 2026
venue: "Proceedings of the 20th International Workshop on Semantic Evaluation (2026)"
source: "https://aclanthology.org/2026.semeval-1.260/"
doi: "10.18653/v1/2026.semeval-1.260"
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/taien-2026-taien-semeval-2026-task-9/"
---
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# Taien at SemEval-2026 Task 9: Multilingual Polarization Detection Using Transformer-based Models

**Authors:** Saida Taien and Palash Hossen · **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-1.260/) · [PDF](https://aclanthology.org/2026.semeval-1.260.pdf) · [DOI 10.18653/v1/2026.semeval-1.260](https://doi.org/10.18653/v1/2026.semeval-1.260) · [OpenAlex](https://openalex.org/W7166871110)

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

## Summary
The authors tackle Subtask 1 of SemEval-2026 Task 9. The task is to decide whether a short text carries ideologically polarized content, a binary judgment that differs from ordinary sentiment analysis. They use the POLAR dataset, which contains social-media and news texts on elections, conflicts, gender rights and migration in 22 languages. The languages range from well-resourced ones like English and Chinese to low-resource ones such as Hausa, Odia and Burmese.

They fine-tune XLM-RoBERTa-base and mDeBERTa-v3-base with one shared pipeline for all languages: light Unicode and whitespace cleanup, 128-token inputs, AdamW and 3 epochs. The final prediction averages the two models' class probabilities (soft voting). The system uses no external corpora and no explicit handling of class imbalance.

On the official test sets, the ensemble reaches an average weighted F1 of 0.788 and an accuracy of 0.822. It ranks first for Burmese, but Italian and Odia remain weak. The work matters as a simple, reproducible multilingual baseline, and it shows that implicit, context-dependent polarization is still hard to detect.

## Key points
- **Data size:** Training sets range from about 1,700 texts (Punjabi) to about 7,000 (Swahili). Test sets contain 809 to 3,147 texts per language.
- **Model comparison:** Average weighted F1 is 0.781 for XLM-RoBERTa-base, 0.774 for mDeBERTa-v3-base and 0.788 for the ensemble. The paper reports these as validation scores, yet the same 0.788 is also given as the test average.
- **Leaderboard positions:**
  - Burmese: 1st place (F1 0.8913).
  - Persian: 2nd place.
  - Swahili: 3rd place.
  - Hausa: 5th place.
  - Weakest languages: Italian (F1 0.5798) and Odia (F1 0.7309).
- **Ablations:**
  - Soft voting adds roughly +0.9 Macro-F1 over majority voting.
  - Selecting the best validation checkpoint beats keeping last-epoch weights.
- **Error analysis:** Errors are mostly false negatives, caused by non-literal phrasing, implicit framing and missing discourse context.
- **Code:** Available on GitHub at saida-taien/Multilingual-Polarization-22lang.

*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

- [University of Chittagong](https://md.research.elamur.ai/free/en/universities/university-of-chittagong/) — University, Chittagong, Bangladesh · 4 works · 5 citations

Also on the byline: [BGC Trust University Bangladesh](https://ror.org/05s2ecw65).

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## Cite

Cite as: Saida Taien and Palash Hossen. 2026. Taien at SemEval-2026 Task 9: Multilingual Polarization Detection Using Transformer-based Models. 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{taien2026taien,
  title = {{Taien at SemEval-2026 Task 9: Multilingual Polarization Detection Using Transformer-based Models}},
  author = {Saida Taien and Palash Hossen},
  booktitle = {Proceedings of the 20th International Workshop on Semantic Evaluation (2026)},
  year = {2026},
  doi = {10.18653/v1/2026.semeval-1.260},
  url = {https://doi.org/10.18653/v1/2026.semeval-1.260}
}
```

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## References

21 references, 5 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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*Updated 31 October 2026*