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

arXiv: https://arxiv.org/abs/2211.05995 · HTML: https://mlsanity.com/paper/2211.05995/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 7 references that are in the map.
- First posted on arXiv 11 Nov 2022.

## Builds on (most cited first, 5 of 5)

- [BERT for Joint Intent Classification and Slot Filling](https://mlsanity.com/paper/1902.10909/) (2019)
- [Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling](https://mlsanity.com/paper/1609.01454/) (2016)
- [Joint Slot Filling and Intent Detection via Capsule Neural Networks](https://mlsanity.com/paper/1812.09471/) (2018)
- [A Bi-model based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling](https://mlsanity.com/paper/1812.10235/) (2018)
- [CONDA: a CONtextual Dual-Annotated dataset for in-game toxicity understanding and detection](https://mlsanity.com/paper/2106.06213/) (2021)

## Built on by (most cited first, 2 of 2)

- [Large Language Models and Games: A Survey and Roadmap](https://mlsanity.com/paper/2402.18659/) (2024)
- [In-game Toxic Detection: Bi-directional Representations with Attention Residuals](https://mlsanity.com/paper/2609.34584/) (2026)

## Abstract (arXiv)

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