检测可能损害公司声誉的敏感话题上的不适当消息
Detecting Inappropriate Messages on Sensitive Topics that Could Harm a Company's Reputation
摘要：
并不是所有的话题在毒性方面都是同样“易燃”的：与政治或性少数群体的讨论相比，关于海龟或捕鱼的平静讨论更不容易引发不适当的有毒对话。我们定义了一组敏感的主题，可以产生不适当的和有毒的消息，并描述了收集和标记数据集的适当性的方法。虽然用户生成的数据中的毒性已经得到了很好的研究，但我们的目标是定义一个更细粒度的不适当概念。不恰当的核心是它会损害演讲者的声誉。这在两个方面与毒性不同：（i）不适当是与主题相关的，（ii）不适当的信息不是毒性，但仍然不可接受。我们收集并发布了两个俄语数据集：一个主题标记数据集和一个适当标记数据集。我们还发布了基于这些数据训练的预训练分类模型。
英文摘要：
Not all topics are equally "flammable" in terms of toxicity: a calm discussion of turtles or fishing less often fuels inappropriate toxic dialogues than a discussion of politics or sexual minorities. We define a set of sensitive topics that can yield inappropriate and toxic messages and describe the methodology of collecting and labeling a dataset for appropriateness. While toxicity in user-generated data is well-studied, we aim at defining a more fine-grained notion of inappropriateness. The core of inappropriateness is that it can harm the reputation of a speaker. This is different from toxicity in two respects: (i) inappropriateness is topic-related, and (ii) inappropriate message is not toxic but still unacceptable. We collect and release two datasets for Russian: a topic-labeled dataset and an appropriateness-labeled dataset. We also release pre-trained classification models trained on this data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
