Russian Sensitive Topics

🚀 俄语敏感话题分类模型

本模型用于对俄语敏感话题进行分类，可预测18种敏感话题的组合。它基于扩展的俄语敏感话题数据集进行训练，能助力检测可能损害公司声誉的不当信息。

🚀 快速开始

该模型可预测文章中描述的18种敏感话题的组合。你可以在此处找到使用该模型的详细步骤说明。

✨ 主要特性

• 针对性训练：在俄语敏感话题数据集上进行训练，能精准识别特定敏感话题。
• 多话题预测：可预测18种敏感话题的组合，满足多样化检测需求。

📚 详细文档

模型概述

此模型在俄语敏感话题数据集上进行训练。敏感话题的概念在这篇文章中有详细描述，该文章于EACL - 2021会议的波罗的海 - 斯拉夫语自然语言处理研讨会上发表。需注意，文章描述的是数据集的第一版，而模型是基于扩展版本的数据集进行训练的，该扩展数据集已在我们的GitHub或kaggle上开源。数据集的特性与文章中描述的一致，仅规模有所不同。

评估指标

数据集部分样本为手动标注，部分为半自动标注，更多信息可查看我们的文章。我们仅在手动标注的数据部分测试了分类器的性能，因此部分话题在测试集中的代表性不足。

📄 许可证

本项目采用知识共享署名 - 非商业性使用 - 相同方式共享 4.0 国际许可协议。

🔖 引用

如果您觉得这个仓库有帮助，请引用我们的论文：

@inproceedings{babakov-etal-2021-detecting,
    title = "Detecting Inappropriate Messages on Sensitive Topics that Could Harm a Company{'}s Reputation",
    author = "Babakov, Nikolay  and
      Logacheva, Varvara  and
      Kozlova, Olga  and
      Semenov, Nikita  and
      Panchenko, Alexander",
    booktitle = "Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing",
    month = apr,
    year = "2021",
    address = "Kiyv, Ukraine",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2021.bsnlp-1.4",
    pages = "26--36",
    abstract = "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 labelling 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-labelled dataset and an appropriateness-labelled dataset. We also release pre-trained classification models trained on this data.",
}