Papers by Nicolas Grenon-Godbout
Towards Detecting Contextual Real-Time Toxicity for In-Game Chat (2023.findings-emnlp)
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| Challenge: | ToxBuster is a simple and scalable model that reliably detects toxic content in real-time for a line of chat by including chat history and metadata. |
| Approach: | They propose a model that detects toxic content in real-time for a line of chat by including chat history and metadata. |
| Outcome: | The proposed model outperforms conventional toxicity models across popular multiplayer games including Rainbow Six Siege, For Honor, and DOTA 2 and 6% of unreported toxic players can be proactively moderated. |
Unveiling Identity Biases in Toxicity Detection : A Game-Focused Dataset and Reactivity Analysis Approach (2023.emnlp-industry)
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| Challenge: | Existing datasets focused on gender or racial biases are not designed for the gaming industry, a concern for models built for toxicity detection in videogames’ written chat. |
| Approach: | They propose to use reactivity analysis to highlight oversensitive terms using a language model developed by Ubisoft for toxicity detection on videogame’s written chat and Perspective API to generate a list of terms that trigger the models to varying degrees. |
| Outcome: | The proposed model can detect and amplify identity biases in annotated language models and is compared with a language model developed by Ubisoft for toxicity detection on videogames’ written chat and Perspective API. |