In-Game Toxic Language Detection: Shared Task and Attention Residuals (Student Abstract)

Authors

  • Yuanzhe Jia University of Sydney
  • Weixuan Wu University of Sydney
  • Feiqi Cao University of Sydney
  • Soyeon Caren Han University of Sydney

DOI:

https://doi.org/10.1609/aaai.v37i13.26979

Keywords:

Toxic Lanaguage Detection, Slot Filling, Hate Speech Detection

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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Published

2024-07-15

How to Cite

Jia, Y., Wu, W., Cao, F., & Han, S. C. (2024). In-Game Toxic Language Detection: Shared Task and Attention Residuals (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16238-16239. https://doi.org/10.1609/aaai.v37i13.26979

Issue

Vol. 37 No. 13: AAAI-23 Special Programs, IAAI-23, EAAI-23, Student Papers and Demonstrations

Section

AAAI Student Abstract and Poster Program