Rethinking Offensive Text Detection as a Multi-Hop Reasoning Problem. (arXiv:2204.10521v1 [cs.CL])

We introduce the task of implicit offensive text detection in dialogues,
where a statement may have either an offensive or non-offensive interpretation,
depending on the listener and context. We argue that reasoning is crucial for
understanding this broader class of offensive utterances and release SLIGHT, a
dataset to support research on this task. Experiments using the data show that
state-of-the-art methods of offense detection perform poorly when asked to
detect implicitly offensive statements, achieving only ${\sim} 11\%$ accuracy.

In contrast …

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