  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.
