BRUMS at SemEval-2020 Task 12 : Transformer based Multilingual Offensive Language Identification in Social Media
This work addresses the problem of detecting offensive content in social media for multiple languages, but it is incremental as it applies existing transformer methods to new multilingual data.
The paper tackled multilingual offensive language identification in social media by developing a transformer-based model, achieving acceptable evaluation scores across Arabic, Danish, English, Greek, and Turkish datasets.
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.