MUDES: Multilingual Detection of Offensive Spans

2021/02/18 by Ranasinghe, Tharindu, Zampieri, Marcos · 3 citations
#Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG)

paper · doi:10.48550/arxiv.2102.09665

Abstract

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.

Cited by

• Evaluating Open-Source Vision-Language Models for Multimodal Sarcasm Detection
• Language, Culture, and Ideology: Personalizing Offensiveness Detection in Political Tweets with Reasoning LLMs
• WATCHED: A Web AI Agent Tool for Combating Hate Speech by Expanding Data

Related

• The performance of multiple language models in identifying offensive language on social media
• WLV-RIT at SemEval-2021 Task 5: A Neural Transformer Framework for\n Detecting Toxic Spans
• An Exploration of State-of-the-art Methods for Offensive Language Detection
• Offensive Language Detection: A Comparative Analysis
• Findings of the Shared Task on Offensive Span Identification from Code-Mixed Tamil-English Comments