@inproceedings{shi-etal-2026-syuhhh,
    title = "syuhhh@{EEUCA} 2026: A Three-Stage Progressive Training Framework for Fine-Grained Toxicity Detection in Online Gaming Communities",
    author = "Shi, Yuhao  and
      Wang, Yu  and
      Zhao, Shengjie",
    editor = {H{\"u}rriyeto{\u{g}}lu, Ali  and
      Thapa, Surendrabikram  and
      Tanev, Hristo},
    booktitle = "Proceedings of the 9th Workshop on Event Extraction and Understanding: Challenges and Applications ({EEUCA} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.eeuca-1.17/",
    pages = "161--168",
    ISBN = "979-8-89176-402-6",
    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."
}