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Machine Learning Summit: Aegis Engine: Building Multi-modal Moderation System in NetEase Games

Yunbo Peng  (Senior AI Engineer, NetEase Games AI Lab)

Pass Type: All Access Pass, Summits Pass - Get your pass now!

Topic: Programming

Format: Session

Vault Recording: TBD

Audience Level: No

User-generated content (UGC) and social interaction greatly improve player participation and game entertainment. To keep the games free from inappropriate content, such as eroticism, violence, spamming, abuse, etc., building a content moderation system is of vital importance. However, manual moderation is time-consuming, laborious and costly.
This session introduces the Aegis Engine, a novel multi-modal content moderation system which applies deep learning techniques for not only text but also unstructured image and audio data. The engine contains three sub-systems. Image sub-system exploits fine-grained recognition and real-time OCR algorithms. Audio sub-system is built upon keyword-enhanced and noise-robust speech recognition. Text sub-system contains word embedding based inappropriate content mining method. The Aegis Engine processes more than 7,000,000 images and 100,000 hours of audio data every day, covering almost all the games in NetEase Games such as Knives Out, Onmyoji, and LifeAfter.


Attendees can learn how to apply latest deep learning techniques to build a multi-modal moderation system with game data. Details of how the Aegis Engine is built, used and maintained are also shared in the presentation.

Intended Audience

The presentation is for anyone who is interested in building a healthy in-game community for multiplayer online games or wants to apply AI to build a moderation system in online games. Basic knowledge of machine learning and deep learning is preferred but not required.