论文标题
在阿拉伯社交媒体上进行厌女症识别和分类的深度多任务模型
Deep Multi-Task Models for Misogyny Identification and Categorization on Arabic Social Media
论文作者
论文摘要
社交媒体平台上有毒内容的普遍性，例如仇恨言论，冒犯性语言和厌女症，给我们的相互联系的社会带来了严重的挑战。这些具有挑战性的问题引起了自然语言处理（NLP）社区的广泛关注。在本文中，我们将提交的系统介绍给第一个阿拉伯语厌女症识别共享任务。我们研究了三个多任务学习模型及其单任务。为了编码输入文本，我们的模型依赖于预先训练的Marbert语言模型。总体获得的结果表明，我们所有提交的模型均在厌女症识别和分类任务中都取得了最佳性能（排名前三的提交）。
The prevalence of toxic content on social media platforms, such as hate speech, offensive language, and misogyny, presents serious challenges to our interconnected society. These challenging issues have attracted widespread attention in Natural Language Processing (NLP) community. In this paper, we present the submitted systems to the first Arabic Misogyny Identification shared task. We investigate three multi-task learning models as well as their single-task counterparts. In order to encode the input text, our models rely on the pre-trained MARBERT language model. The overall obtained results show that all our submitted models have achieved the best performances (top three ranked submissions) in both misogyny identification and categorization tasks.
