---
title: "PolAR Bears at SemEval-2026 Task 9: Parameter-Efficient"
description: "The work offers an inexpensive single-GPU recipe and exposes its limits for fine-grained tasks in… 14 references, cited by 1 paper here."
authors: ["Vinay Ulli", "Jyoti Kumari"]
year: 2026
venue: "Proceedings of the 20th International Workshop on Semantic Evaluation (2026)"
source: "https://aclanthology.org/2026.semeval-1.279/"
doi: "10.18653/v1/2026.semeval-1.279"
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/ulli-2026-polar-bears-semeval-2026-task/"
---
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# PolAR Bears at SemEval-2026 Task 9: Parameter-Efficient Fine-Tuning and Cross-Lingual Augmentation for Multilingual Polarization Detection

**Authors:** [Vinay Ulli](https://md.research.elamur.ai/free/en/authors/vinay-babu-ulli/) and [Jyoti Kumari](https://md.research.elamur.ai/free/en/authors/jyoti-kumari/) · **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-1.279/) · [PDF](https://aclanthology.org/2026.semeval-1.279.pdf) · [DOI 10.18653/v1/2026.semeval-1.279](https://doi.org/10.18653/v1/2026.semeval-1.279) · [OpenAlex](https://openalex.org/W7166848998)

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

## Summary
The authors address online polarization detection in four under-resourced Indian languages (Hindi, Bengali, Telugu and Odia) within SemEval-2026 Task 9. The task has three parts: binary polarization detection, multi-label classification of target groups, and multi-label identification of rhetorical manifestations. Labelled data is scarce, so they machine-translated every training item with IndicTrans2 into the other three languages and kept the original labels, roughly quadrupling the training set. One framework handles all three subtasks. Qwen3-4B-Instruct receives ChatML prompts that list the relevant categories. Instead of generating answers, the model's final-layer hidden states are mean-pooled and passed to a linear classifier, and the model is adapted cheaply with QLoRA. Binary detection worked well, but the multi-label subtasks performed strongly only for Hindi. The authors attribute this to three causes: Qwen's Hindi-heavy pre-training, translation flattening rhetorical subtlety, and a fixed 0.5 decision threshold. They ran no baselines, so the augmentation's own contribution remains unmeasured. The work offers an inexpensive single-GPU recipe and exposes its limits for fine-grained tasks in lower-resource languages.

## Key points
- **Data augmentation:** IndicTrans2 generated 12 translation directions among the four languages, growing the training corpus from about 11,350 to 43,244 samples. Translation quality was checked only automatically, via bge-m3 cosine similarity, with no human review.
- **Model setup:** Qwen3-4B-Instruct was fine-tuned with QLoRA (4-bit NF4, LoRA r=16, α=32, dropout 0.05 on all attention and MLP projections).
  - Only about 33M parameters (0.81%) were trained, on a single A100 40GB GPU.
  - Training used AdamW with a learning rate of 3e-4 and an effective batch size of 32.
- **Subtask 1 (binary detection):** average Macro F1 of 0.813, ranging from 0.7589 (Odia) to a best of 0.8668 (Telugu).
- **Subtasks 2 and 3 (multi-label):** average Macro F1 was only 0.354 and 0.303. Hindi was the exception, at 0.7008 and 0.7248, while Telugu dropped to 0.1299 in Subtask 2 and Odia to 0.1196 in Subtask 3.
- **Proposed future work:** class-aware threshold tuning and curation of native (non-translated) datasets. Comparisons with mBERT, IndicBERT or an unaugmented baseline are still missing.

*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

- [Banaras Hindu University](https://md.research.elamur.ai/free/en/universities/banaras-hindu-university/) — University, Varanasi, India · 6 works · 260 citations

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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: Vinay Ulli and Jyoti Kumari. 2026. PolAR Bears at SemEval-2026 Task 9: Parameter-Efficient Fine-Tuning and Cross-Lingual Augmentation for Multilingual Polarization Detection. 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{ulli2026polar,
  title = {{PolAR Bears at SemEval-2026 Task 9: Parameter-Efficient Fine-Tuning and Cross-Lingual Augmentation for Multilingual Polarization Detection}},
  author = {Vinay Ulli and Jyoti Kumari},
  booktitle = {Proceedings of the 20th International Workshop on Semantic Evaluation (2026)},
  year = {2026},
  doi = {10.18653/v1/2026.semeval-1.279},
  url = {https://doi.org/10.18653/v1/2026.semeval-1.279}
}
```

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## References

14 references, 12 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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3. Thomas Davidson, Dana Warmsley, Michael Macy, and Ingmar Weber. 2017. [Automated hate speech detection and the problem of offensive language](https://md.research.elamur.ai/free/en/papers/davidson-2017-automated-hate-speech-detection-problem/). In Proceedings of the International AAAI Conference on Web and Social Media, volume 11, pages 512– 515.
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6. Jay Gala, Diptesh Kanojia, Sudhanshu Bhatia, Hitesh Doshi, Sumanth Doddapaneni, and 1 others. 2023. [IndicTrans2: Towards high-quality and accessible machine translation models for all 22 scheduled indian languages](https://md.research.elamur.ai/free/en/papers/gala-2023-indictrans2-towards-high-quality-accessible/). Transactions of the Association for Computational Linguistics, 12.
7. Edward J Hu, Yelong Shen, Phil Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2022. [LoRA: Low-rank adaptation of large language models](https://md.research.elamur.ai/free/en/papers/hu-2021-lora-low-rank-adaptation-large/). In International Conference on Learning Representations.
8. Divyanshu Kakwani, Anoop Kunchukuttan, Satish Golla, Gokul N.C., Pushpak Bhattacharyya, Mitesh M. Khapra, and Pratyush Kumar. 2020. [IndicNLPSuite: Monolingual corpora, evaluation benchmarks and pre-trained multilingual language models for indian languages](https://md.research.elamur.ai/free/en/papers/kakwani-2020-indicnlpsuite-monolingual-corpora-evaluation-benchmarks/). In Findings of the Association for Computat…
9. Julia Mendelsohn, Vinodkumar Prabhakaran, and Dan Jurafsky. 2021. A framework for the computational linguistic analysis of dehumanization. In Proceedings of Frontiers in NLP. [find](https://scholar.google.com/scholar?q=Julia+Mendelsohn%2C+Vinodkumar+Prabhakaran%2C+and+Dan+Jurafsky%2E+2021%2E+A+framework+for+the+computational+linguistic+analysis+of+dehumanization%2E+In+Proceedings+of+Frontiers+in+NLP%2E)
10. Niklas Muennighoff. 2022. [Sgpt: Gpt sentence embeddings for semantic search](https://md.research.elamur.ai/free/en/papers/muennighoff-2022-sgpt-gpt-sentence-embeddings-semantic/). arXiv preprint arXiv:2202.08904.
11. Usman Naseem, Robert Geislinger, Juan Ren, Sarah Kohail, Rudy Garrido Veliz, P Sam Sahil, Yiran Zhang, Marco Antonio Stranisci, Idris Abdulmumin, "Ozge Alacam, Cengiz Acar"urk, Aisha Jabr, Saba Anwar, Abinew Ali Ayele, Elena Tutubalina, Aung Kyaw Htet, Xintong Wang, Surendrabikram Thapa, Tanmoy Chak… — [SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization](https://md.research.elamur.ai/free/en/papers/naseem-2026-semeval-2026-task-9-detecting/)
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14. An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, and 1 others. 2025. [Qwen3 technical report](https://md.research.elamur.ai/free/en/papers/yang-2025-qwen3-technical-report/). arXiv preprint arXiv:2505.09388.

*Updated 31 October 2026*