SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)

Abstract

We present the results and the main findings of SemEval-2019 Task 6 on Identifying and Categorizing Offensive Language in Social Media (OffensEval). The task was based on a new dataset, the Offensive Language Identification Dataset (OLID), which contains over 14,000 English tweets. It featured three sub-tasks. In sub-task A, the goal was to discriminate between offensive and non-offensive posts. In sub-task B, the focus was on the type of offensive content in the post. Finally, in sub-task C, systems had to detect the target of the offensive posts. OffensEval attracted a large number of participants and it was one of the most popular tasks in SemEval-2019. In total, about 800 teams signed up to participate in the task, and 115 of them submitted results, which we present and analyze in this report.

arXiv commentProceedings of the International Workshop on Semantic Evaluation (SemEval)

References in corpus (9)

• Distributed Representations of Words and Phrases and their Compositionality
• Enriching Word Vectors with Subword Information
• Convolutional Neural Networks for Toxic Comment Classification
• Bidirectional Recurrent Models for Offensive Tweet Classification
• An Exploration of State-of-the-art Methods for Offensive Language Detection
• Offensive Language Analysis using Deep Learning Architecture
• Combination of multiple Deep Learning architectures for Offensive Language Detection in Tweets
• Towards NLP with Deep Learning: Convolutional Neural Networks and Recurrent Neural Networks for Offensive Language Identification in Social Media
• Absit invidia verbo: Comparing Deep Learning methods for offensive language

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• Anticipating Safety Issues in E2E Conversational AI: Framework and Tooling
• Leveraging Affective Bidirectional Transformers for Offensive Language Detection
• On Analyzing Antisocial Behaviors Amid COVID-19 Pandemic
• Fine-tuning of Pre-trained Transformers for Hate, Offensive, and Profane Content Detection in English and Marathi
• TAR on Social Media: A Framework for Online Content Moderation
• Don't Patronize Me! An Annotated Dataset with Patronizing and Condescending Language towards Vulnerable Communities
• UoB at SemEval-2021 Task 5: Extending Pre-Trained Language Models to Include Task and Domain-Specific Information for Toxic Span Prediction
• Offensive Language Detection: A Comparative Analysis
• General Purpose Text Embeddings from Pre-trained Language Models for Scalable Inference
• MIPT-NSU-UTMN at SemEval-2021 Task 5: Ensembling Learning with Pre-trained Language Models for Toxic Spans Detection