Revisiting Contextual Toxicity Detection in Conversations
Understanding toxicity in user conversations is undoubtedly an important
problem. As it has been argued in previous work, addressing "covert" or
implicit cases of toxicity is particularly hard and requires context. Very few
previous studies have analysed the influence of conversational context in human
perception or in automated detection models. We dive deeper into both these
directions. We start by analysing existing contextual datasets and come to the
conclusion that toxicity labelling by humans is in general influenced by the
conversational structure, polarity and topic of the context. We then propose to
bring these findings into computational detection models by introducing (a)
neural architectures for contextual toxicity detection that are aware of the
conversational structure, and (b) data augmentation strategies that can help
model contextual toxicity detection. Our results have shown the encouraging
potential of neural architectures that are aware of the conversation structure.
We have also demonstrated that such models can benefit from synthetic data,
especially in the social media domain.