AdelaideCyC at SemEval-2020 Task 12: Ensemble of Classifiers for Offensive Language Detection in Social Media

Abstract

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.

Enhancing the Identification of Cyberbullying through Participant Roles

“…Instead of building new models, we extend an ensemble model originally designed by the authors (Herath et al, 2020) for SemEval-2020 Task on offensive language identification (Zampieri et al, 2020), to classify posts in the current dataset. The reused ensemble model (Herath et al, 2020) was built using three single classifiers, each based on DistilBERT (Sanh et al, 2019), a lighter, faster version of BERT (Devlin et al, 2018). Each of the single classifiers A, B, and C was trained on a Twitter dataset containing Tweets annotated as offensive ('OFF') or non-offensive('NOT') posts.…”

“…As discussed in section 3.1, our cyberbullying classification experiments extended an ensemble model (refer as 'OffensEval ensemble' hereafter) based on DistilBERT developed by authors for SemEval 2020 challenge (Herath et al, 2020). To test the performance of OffensEval ensemble on ASKfm dataset, we constructed three test datasets.…”