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        <title>FNLP412@EEUCA 2026: Understanding Toxic Behavioral Intent in Gaming Chat Logs using Transfer Learning and Synthetic Data Augmentation</title>
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        <name type="personal">
            <namePart type="given">Ali</namePart>
            <namePart type="family">Hürriyetoğlu</namePart>
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        <name type="personal">
            <namePart type="given">Surendrabikram</namePart>
            <namePart type="family">Thapa</namePart>
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        <name type="personal">
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    <abstract>Our paper explores several machine learning methods for detecting toxic language in gaming-related chat utterances. We start with the GameTox dataset, perform some data preprocessing and augment the minority classes with LLM-generated synthetic data. We then set a baseline using a classic Logistic Regression model and continue to explore severalapproaches to surpassing it, by leveraging the leading multilingual transformer models (XLM-RoBERTa and DeBERTa-V3) to classify our test data. We achieve a top result of 0.6725 Macro-F1 (2nd place on shared task leaderboard) using a MDeBERTa-V3 model which we pretrained on the Jigsaw dataset for 1 epoch and then fine-tuned on our GameTox data for 5 epochs.</abstract>
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%0 Conference Proceedings
%T FNLP412@EEUCA 2026: Understanding Toxic Behavioral Intent in Gaming Chat Logs using Transfer Learning and Synthetic Data Augmentation
%A Radulescu, Mihai Radu
%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 radulescu-2026-fnlp412
%X Our paper explores several machine learning methods for detecting toxic language in gaming-related chat utterances. We start with the GameTox dataset, perform some data preprocessing and augment the minority classes with LLM-generated synthetic data. We then set a baseline using a classic Logistic Regression model and continue to explore severalapproaches to surpassing it, by leveraging the leading multilingual transformer models (XLM-RoBERTa and DeBERTa-V3) to classify our test data. We achieve a top result of 0.6725 Macro-F1 (2nd place on shared task leaderboard) using a MDeBERTa-V3 model which we pretrained on the Jigsaw dataset for 1 epoch and then fine-tuned on our GameTox data for 5 epochs.
%U https://aclanthology.org/2026.eeuca-1.10/
%P 96-103

Markdown (Informal)

[FNLP412@EEUCA 2026: Understanding Toxic Behavioral Intent in Gaming Chat Logs using Transfer Learning and Synthetic Data Augmentation](https://aclanthology.org/2026.eeuca-1.10/) (Radulescu, EEUCA 2026)

• FNLP412@EEUCA 2026: Understanding Toxic Behavioral Intent in Gaming Chat Logs using Transfer Learning and Synthetic Data Augmentation (Radulescu, EEUCA 2026)

ACL

• Mihai Radu Radulescu. 2026. FNLP412@EEUCA 2026: Understanding Toxic Behavioral Intent in Gaming Chat Logs using Transfer Learning and Synthetic Data Augmentation. In Proceedings of the 9th Workshop on Event Extraction and Understanding: Challenges and Applications (EEUCA 2026), pages 96–103, San Diego, California, USA. Association for Computational Linguistics.