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• W4313459377 abstract "Abstract To proactively offer social media users a safe online experience, there is a need for systems that can detect harmful posts and promptly alert platform moderators. In order to guarantee the enforcement of a consistent policy, moderators are provided with detailed guidelines. In contrast, most state-of-the-art models learn what abuse is from labeled examples and as a result base their predictions on spurious cues, such as the presence of group identifiers, which can be unreliable. In this work we introduce the concept of policy-aware abuse detection, abandoning the unrealistic expectation that systems can reliably learn which phenomena constitute abuse from inspecting the data alone. We propose a machine-friendly representation of the policy that moderators wish to enforce, by breaking it down into a collection of intents and slots. We collect and annotate a dataset of 3,535 English posts with such slots, and show how architectures for intent classification and slot filling can be used for abuse detection, while providing a rationale for model decisions.1" @default.
• W4313459377 created "2023-01-06" @default.
• W4313459377 creator A5023817571 @default.
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• W4313459377 date "2022-01-01" @default.
• W4313459377 modified "2023-10-01" @default.
• W4313459377 title "Explainable Abuse Detection as Intent Classification and Slot Filling" @default.
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• W4313459377 doi "https://doi.org/10.1162/tacl_a_00527" @default.
• W4313459377 hasPublicationYear "2022" @default.
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