Rethinking Offensive Text Detection as a Multi-Hop Reasoning Problem

Anonymous

Abstract: 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 create Mh-RIOT ($M$ulti-hop $R$easoning $I$mplicitly $O$ffensive $T$ext Dataset), to support research on this task. Experiments using the dataset show that state-of-the-art methods of offense detection perform poorly when asked to detect implicitly offensive statements, achieving only $<11$ accuracy.

Paper Type: long