In this paper, we describe our approach to utilize pre-trained BERT models with Convolutional Neural Networks for sub-task A of the Multilingual Offensive Language Identification shared task (OffensEval 2020), which is a part of the SemEval 2020. We show that combining CNN with BERT is better than using BERT on its own, and we emphasize the importance of utilizing pre-trained language models for downstream tasks. Our system, ranked 4th with macro averaged F1-Score of 0.897 in Arabic, 4th with score of 0.843 in Greek, and 3rd with score of 0.814 in Turkish. Additionally, we present ArabicBERT, a set of pre-trained transformer language models for Arabic that we share with the community.
@article{arxiv.2007.13184,
title = {KUISAIL at SemEval-2020 Task 12: BERT-CNN for Offensive Speech Identification in Social Media},
author = {Ali Safaya and Moutasem Abdullatif and Deniz Yuret},
journal= {arXiv preprint arXiv:2007.13184},
year = {2020}
}
Comments
to be published in the proceedings of the 14th International Workshop on Semantic Evaluation (SemEval2020), Association for Computational Linguistics (ACL)