MULTILINGUAL, CONTEXT-AWARE MACHINE LEARNING MODEL CONFIGURED FOR PROFANITY DETECTION AND MITIGATION

Certain aspects of the disclosure relate to profanity detection and mitigation. A method generally includes training a machine learning (ML) model using labeled training data instances by, for each training data instance: providing the tokens of the respective training data instance to an input layer of the ML model; receiving a first output for each token of the respective training data instance classifying the respective token as a profanity-containing or a non-profanity-containing token; receiving a second output for the respective training data instance classifying the respective training data instance as a profanity-containing or a non-profanity-containing instance; determining a loss value based on the first output for each token and the second output using a loss function comprising a regularization term configured to increase loss based on disagreement between the first output for each token and the second output; and modifying parameter(s) of the ML model based on the loss value.