An Exploration of State-of-the-art Methods for Offensive Language Detection

Uglow, Harrison, Zlocha, Martin, Zmyślony, Szymon

arXiv.org Machine Learning 

We provide a comprehensive investigation of different custom and off-the-shelf architectures as well as different approaches to generating feature vectors for offensive language detection. We also show that these approaches work well on small and noisy datasets such as on the Offensive Language Identification Dataset (OLID), so it should be possible to use them for other applications.

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