Decipherment for Adversarial Offensive Language Detection

WS 2018 Zhelun WuNishant KambhatlaAnoop Sarkar

Automated filters are commonly used by online services to stop users from sending age-inappropriate, bullying messages, or asking others to expose personal information. Previous work has focused on rules or classifiers to detect and filter offensive messages, but these are vulnerable to cleverly disguised plaintext and unseen expressions especially in an adversarial setting where the users can repeatedly try to bypass the filter... (read more)

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