In-game Toxic Language Detection: Shared Task and Attention Residuals
This addresses toxicity detection for online gaming communities, presenting an incremental improvement with a new shared task and model.
The paper tackles the problem of detecting toxic language in short in-game chats by establishing a shared task with real-world data and proposing a model for toxic language token tagging, achieving results through a publicly available framework.
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 relevant code is publicly available on GitHub: https://github.com/Yuanzhe-Jia/In-Game-Toxic-Detection