The interest in offensive content identification in social media has grown substantially in recent years. Previous work has dealt mostly with post level annotations. However, identifying offensive spans is useful in many ways. To help coping with this important challenge, we present MUDES, a multilingual system to detect offensive spans in texts. MUDES features pre-trained models, a Python API for developers, and a user-friendly web-based interface. A detailed description of MUDES' components is presented in this paper.
翻译:近年来,社会媒体对冒犯性内容识别的兴趣大幅增长,以往的工作大多涉及后级说明,然而,确定冒犯性内容的跨度在许多方面是有用的,为了帮助应对这一重要挑战,我们向妇女、教育和社会发展部介绍一个多语种系统,用于检测文本中的冒犯性跨度,妇女、教育和社会发展部的特点是预先培训模型、开发商的Python API以及一个方便用户的网络界面,本文详细介绍了妇女、教育和发展事务部的组成部分。