Revisiting The Offensive Text Detection Problem with a Chain-of-Reasoning Approach

We introduce the task of implicit offensive-text detection (OTD) in dialogues, where a statement may have either an offensive or nonoffensive 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 test dataset to support research on this topic. Experiments using the data show that state-of-the-art methods for offense detection perform poorly when tasked with detecting implicitly offensive statements, achieving only ∼11% accuracy. In contrast to the existing OTD datasets, SLIGHT features human-annotated chains of reasoning that describe the mental process through which an offensive interpretation can be reached from an ambiguous statement. We explore the potential of a multihop reasoning approach, by utilizing the existing entailment models to evaluate the probabilities of these chains. Our results demonstrate that reasoning through chains can yield performances better than that of a baseline entailment setting without chains. Furthermore, the analysis of the chains provides insights into the human interpretation process and emphasizes the importance of incorporating additional commonsense knowledge.