In-game Toxic Language Detection: Shared Task and Attention Residuals

• URL: http://arxiv.org/abs/2211.05995v2
• Date: Mon, 14 Nov 2022 04:20:18 GMT
• Title: In-game Toxic Language Detection: Shared Task and Attention Residuals
• Authors: Yuanzhe Jia, Weixuan Wu, Feiqi Cao, Soyeon Caren Han
• Abstract summary: We describe how the in-game toxic language shared task has been established using the real-world in-game chat data. In addition, we propose and introduce the model/framework for toxic language token tagging (slot filling) from the in-game chat.
• Score: 1.9218741065333018
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In-game toxic language becomes the hot potato in the gaming industry and community. There have been several online game toxicity analysis frameworks and models proposed. However, it is still challenging to detect toxicity due to the nature of in-game chat, which has extremely short length. In this paper, we describe how the in-game toxic language shared task has been established using the real-world in-game chat data. In addition, we propose and introduce the model/framework for toxic language token tagging (slot filling) from the in-game chat. The data and code will be released.

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