  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 to existing offensive text detection datasets, SLIGHT features
human-annotated chains of reasoning which describe the mental process by which
an offensive interpretation can be reached from each ambiguous statement. We
explore the potential for a multi-hop reasoning approach by utilizing existing
entailment models to score the probability of these chains and show that even
naive reasoning models can yield improved performance in most situations.
Furthermore, analysis of the chains provides insight into the human
interpretation process and emphasizes the importance of incorporating
additional commonsense knowledge.
