AdelaideCyC at SemEval-2020 Task 12: Ensemble of classifiers for offensive language detection in social media

• Main Authors: HERATH, Mahen, ATAPATTU, Thushari, DUNG, Hoang Anh, TREUDE, Christoph, FALKNER, Katrina
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Social Media; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/8931; https://ink.library.smu.edu.sg/context/sis_research/article/9934/viewcontent/semeval20.pdf
• Institution: Singapore Management University

This paper describes the systems our team (AdelaideCyC) has developed for SemEval Task 12 (OffensEval 2020) to detect offensive language in social media. The challenge focuses on three subtasks – offensive language identification (subtask A), offense type identification (subtask B), and offense target identification (subtask C). Our team has participated in all the three subtasks. We have developed machine learning and deep learning-based ensembles of models. We have achieved F1-scores of 0.906, 0.552, and 0.623 in subtask A, B, and C respectively. While our performance scores are promising for subtask A, the results demonstrate that subtask B and C still remain challenging to classify.