Detection and Identification of Obfuscated Obscene Language with Character Level Transformers
by Tobias Renwick and Denilson Barbosa
Published onJun 08, 2021
Detection and Identification of Obfuscated Obscene Language with Character Level Transformers
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Abstract
We present O-Norm (Offense Normalizer), a system for normalizing offensive words and phrases by reversing user-created obfuscations. O-Norm is intended to be a preprocessing tool for pipelines that run on user-created text, particularly those tasks where obscenities may be of significant value such as sentiment, toxicity, and abuse detection. O-Norm is constructed with a purely generated, context-free dataset derived from a curse dictionary. The generative dataset allows O-Norm to be flexible to the addition of new words as language adapts, and also allows it to be readily retrained in other Latin alphabet-based languages as it does not require manual annotations to train. O-Norm is based on a character-level, transformer network which attempts de-obfuscation only on out-of-vocabulary (OOV) tokens. In an 80/20 train-test split, O-Norm achieves an F1 score of 89.6% over 141 curses in a generated dataset with 2.16 million unique training points. An inspection of O-Norm’s output on a sample of social media posts from Kaggle’s Jigsaw corpus reveals accuracy of 95.7% on de-obfuscating transformations in toxic user-created text.
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