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

TL;DR

This paper introduces a shared task for detecting toxic language in in-game chat and proposes a novel model with attention residuals for token tagging, addressing the challenge of short chat messages.

Contribution

It establishes a shared task dataset and presents a new model framework for toxic language detection in short in-game chats, enhancing detection accuracy.

Findings

Effective detection of toxic language in short chat messages.

The proposed model outperforms existing methods in accuracy.

Publicly available code facilitates further research.

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 relevant code is publicly available on GitHub: https://github.com/Yuanzhe-Jia/In-Game-Toxic-Detection