论文标题
重新考虑进攻性文本检测作为多跳的推理问题
Rethinking Offensive Text Detection as a Multi-Hop Reasoning Problem
论文作者
论文摘要
我们在对话中介绍了隐式进攻性文本检测的任务，在对话中，根据听众和上下文，声明可能具有进攻性或非犯罪解释。我们认为，推理对于理解这种更广泛的进攻性话语和释放轻微的数据集至关重要，这是一个支持有关此任务的研究的数据集。使用数据的实验表明，当被要求检测隐式进攻性陈述时，最新的进攻检测方法的性能很差，仅实现$ {\ sim} 11 \％$精度。 与现有的进攻性文本检测数据集相反，略有特征是人类宣传的推理链，这些链描述了可以从每个模棱两可的陈述中得出进攻性解释的心理过程。我们通过利用现有的元素模型来评分这些链的概率来探索多跳推理方法的潜力，并表明即使是天真的推理模型也可以在大多数情况下产生改善的性能。此外，对链条的分析提供了对人类解释过程的见解，并强调了纳入其他常识性知识的重要性。
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.
