@inproceedings{bao-etal-2026-ynu,
title = "{YNU}-{HPCC} at {S}em{E}val-2026 Task 9: Hybrid Augmentation and Regularization Strategies for Multilingual Polarization Type Classification",
author = "Bao, Di and
Wang, Jin and
Zhang, Xuejie",
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-1.99/",
pages = "699--705",
ISBN = "979-8-89176-414-9",
abstract = "This paper introduces a system based on fine-tuned pretrained language models, which is constructed for SemEval 2026 Task 9: Multilingual Polarization Type Classification. The task aims to perform multi-label polarization classification on texts covering 22 languages, identifying five types of polarization: political, racial/ethnic, religious, gender/sexual, and others. The main challenges of the task lie in handling uneven data distribution across languages, extreme class imbalance, and the complexity of cross-lingual semantic understanding. To address these challenges, a training framework integrating hybrid augmentation and multi-strategy regularization is proposed. Based on XLM-RoBERTa-large, the framework combines feature-space Mixup augmentation, an asymmetric loss function, adversarial training, and exponential moving average. Multi-label decisions are made through dynamic threshold optimization. Experimental results show that the proposed method achieves a macro-F1 score of 0.48 on the validation set, effectively improving classification performance and generalization capability in multilingual and imbalanced scenarios."
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<abstract>This paper introduces a system based on fine-tuned pretrained language models, which is constructed for SemEval 2026 Task 9: Multilingual Polarization Type Classification. The task aims to perform multi-label polarization classification on texts covering 22 languages, identifying five types of polarization: political, racial/ethnic, religious, gender/sexual, and others. The main challenges of the task lie in handling uneven data distribution across languages, extreme class imbalance, and the complexity of cross-lingual semantic understanding. To address these challenges, a training framework integrating hybrid augmentation and multi-strategy regularization is proposed. Based on XLM-RoBERTa-large, the framework combines feature-space Mixup augmentation, an asymmetric loss function, adversarial training, and exponential moving average. Multi-label decisions are made through dynamic threshold optimization. Experimental results show that the proposed method achieves a macro-F1 score of 0.48 on the validation set, effectively improving classification performance and generalization capability in multilingual and imbalanced scenarios.</abstract>
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%0 Conference Proceedings
%T YNU-HPCC at SemEval-2026 Task 9: Hybrid Augmentation and Regularization Strategies for Multilingual Polarization Type Classification
%A Bao, Di
%A Wang, Jin
%A Zhang, Xuejie
%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 bao-etal-2026-ynu
%X This paper introduces a system based on fine-tuned pretrained language models, which is constructed for SemEval 2026 Task 9: Multilingual Polarization Type Classification. The task aims to perform multi-label polarization classification on texts covering 22 languages, identifying five types of polarization: political, racial/ethnic, religious, gender/sexual, and others. The main challenges of the task lie in handling uneven data distribution across languages, extreme class imbalance, and the complexity of cross-lingual semantic understanding. To address these challenges, a training framework integrating hybrid augmentation and multi-strategy regularization is proposed. Based on XLM-RoBERTa-large, the framework combines feature-space Mixup augmentation, an asymmetric loss function, adversarial training, and exponential moving average. Multi-label decisions are made through dynamic threshold optimization. Experimental results show that the proposed method achieves a macro-F1 score of 0.48 on the validation set, effectively improving classification performance and generalization capability in multilingual and imbalanced scenarios.
%U https://aclanthology.org/2026.semeval-1.99/
%P 699-705
Markdown (Informal)
[YNU-HPCC at SemEval-2026 Task 9: Hybrid Augmentation and Regularization Strategies for Multilingual Polarization Type Classification](https://aclanthology.org/2026.semeval-1.99/) (Bao et al., SemEval 2026)
ACL