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        <title>syuhhh@EEUCA 2026: A Three-Stage Progressive Training Framework for Fine-Grained Toxicity Detection in Online Gaming Communities</title>
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        <namePart type="given">Yuhao</namePart>
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        <dateIssued>2026-07</dateIssued>
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            <title>Proceedings of the 9th Workshop on Event Extraction and Understanding: Challenges and Applications (EEUCA 2026)</title>
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            <namePart type="given">Ali</namePart>
            <namePart type="family">Hürriyetoğlu</namePart>
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            <namePart type="given">Surendrabikram</namePart>
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    <abstract>This paper presents our 1st-place system for the Shared Task on Fine-Grained Toxicity Detection in Online Gaming (GameTox) at the 9th EEUCA Workshop, co-located with ACL 2026. The task targets 6-class fine-grained toxic intent classification on the official GameTox dataset, comprising 53,000 real-world World of Tanks chat utterances. We propose a three-stage progressive training framework built on XLM-RoBERTa-large: (1) gaming domain adaptive MLM pre-training, (2) multilingual toxicity transfer fine-tuning, and (3) supervised contrastive learning (SCL)-enhanced target task tuning. We further incorporate LLM-driven data augmentation and long-tailed class synthesis. Our system achieves a Macro F1 of 0.7041, ranking 1st among 35 teams. Ablation studies validate each module’s contribution, and we release our code to facilitate follow-up research.</abstract>
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%0 Conference Proceedings
%T syuhhh@EEUCA 2026: A Three-Stage Progressive Training Framework for Fine-Grained Toxicity Detection in Online Gaming Communities
%A Shi, Yuhao
%A Wang, Yu
%A Zhao, Shengjie
%Y Hürriyetoğlu, Ali
%Y Thapa, Surendrabikram
%Y Tanev, Hristo
%S Proceedings of the 9th Workshop on Event Extraction and Understanding: Challenges and Applications (EEUCA 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-402-6
%F shi-etal-2026-syuhhh
%X This paper presents our 1st-place system for the Shared Task on Fine-Grained Toxicity Detection in Online Gaming (GameTox) at the 9th EEUCA Workshop, co-located with ACL 2026. The task targets 6-class fine-grained toxic intent classification on the official GameTox dataset, comprising 53,000 real-world World of Tanks chat utterances. We propose a three-stage progressive training framework built on XLM-RoBERTa-large: (1) gaming domain adaptive MLM pre-training, (2) multilingual toxicity transfer fine-tuning, and (3) supervised contrastive learning (SCL)-enhanced target task tuning. We further incorporate LLM-driven data augmentation and long-tailed class synthesis. Our system achieves a Macro F1 of 0.7041, ranking 1st among 35 teams. Ablation studies validate each module’s contribution, and we release our code to facilitate follow-up research.
%U https://aclanthology.org/2026.eeuca-1.17/
%P 161-168

Markdown (Informal)

[syuhhh@EEUCA 2026: A Three-Stage Progressive Training Framework for Fine-Grained Toxicity Detection in Online Gaming Communities](https://aclanthology.org/2026.eeuca-1.17/) (Shi et al., EEUCA 2026)

• syuhhh@EEUCA 2026: A Three-Stage Progressive Training Framework for Fine-Grained Toxicity Detection in Online Gaming Communities (Shi et al., EEUCA 2026)

ACL

• Yuhao Shi, Yu Wang, and Shengjie Zhao. 2026. syuhhh@EEUCA 2026: A Three-Stage Progressive Training Framework for Fine-Grained Toxicity Detection in Online Gaming Communities. In Proceedings of the 9th Workshop on Event Extraction and Understanding: Challenges and Applications (EEUCA 2026), pages 161–168, San Diego, California, USA. Association for Computational Linguistics.