In-game Toxic Detection: Bi-directional Representations with Attention Residuals
Organizations: University of Sydney, Australia
Abstract
In-game toxic language has emerged as a critical concern in the gaming industry and community. While several frameworks and models for online game toxicity analysis have been proposed, detecting toxicity in player chat utterances remains a formidable challenge: stemming not only from the extremely short length of such utterances but also from the heavy reliance on game slang, abbreviations, and domain-specific jargon, which generic language models are poorly suited to recognize. This paper presents a shared task for in-game toxic language detection built upon real-world in-game chat data, and proposes the best-preforming model for the toxic language slot filling: Bi-directional Representations with Attention Residuals (BRAR). Experimental results demonstrate that BRAR effectively captures the global context and outperforms the existing baselines on slot filling.
Figures & tables
| Number of Stacks | F1 | F1(T) | F1(S) | F1(D) |
|---|---|---|---|---|
| 1 layer | 99.9 | 98.6 | 99.4 | 98.1 |
| 2 layers | 99.6 | 98.1 | 99.3 | 96.4 |
| 3 layers | 98.6 | 96.5 | 98.3 | 81.8 |
| Model | F1 | F1(T) | F1(S) | F1(D) |
|---|---|---|---|---|
| RNN-NLU ( 2016 ) | 97.0 | 93.1 | 93.0 | 71.8 |
| Slot-gated ( 2018 ) | 99.1 | 97.8 | 98.2 | 95.2 |
| Inter-BiLSTM ( 2018 ) | 86.5 | 87.1 | 86.9 | 78.8 |
| Capsule NN ( 2019 ) | 99.1 | 97.5 | 98.2 | 94.9 |
| Joint BERT (2019) | 98.9 | 97.2 | 97.9 | 91.4 |
| BRAR (our model) | 99.9 | 98.6 | 99.4 | 98.1 |