In this paper, we describe the team \textit{BRUMS} entry to OffensEval 2: Multilingual Offensive Language Identification in Social Media in SemEval-2020. The OffensEval organizers provided participants with annotated datasets containing posts from social media in Arabic, Danish, English, Greek and Turkish. We present a multilingual deep learning model to identify offensive language in social media. Overall, the approach achieves acceptable evaluation scores, while maintaining flexibility between languages.
翻译:在本文中,我们描述了 " Effensval 2:SemEval-2020社会媒体多语言进攻性语言识别 " 的团队进入 " offensval 2:SemEval-2020社会媒体多语言进攻性语言识别 " 。 " OffensEval " 组织者向与会者提供了附加说明的数据集,其中载有来自社交媒体的阿拉伯文、丹麦文、英文、希腊文和土耳其文文章。我们提出了一个多语言的深层次学习模式,用以识别社交媒体中的冒犯性语言。总体而言,该方法取得了可接受的评价分数,同时保持了不同语言之间的灵活性。