Abstract

This paper presents the results and main findings of the HASOC-2021 Hate/Offensive Language Identification Subtask A. The work consisted of fine-tuning pre-trained transformer networks such as BERT and an ensemble of different models, including CNN and BERT. We have used the HASOC-2021 English 3.8k annotated twitter dataset. We compare current pre-trained transformer networks with and without Masked-Language-Modelling (MLM) fine-tuning on their performance for offensive language detection. Among different BERT MLM fine-tuned BERT-base, BERT-large, and ALBERT outperformed other models; however, BERT and CNN ensemble classifier that applies majority voting outperformed other models, achieving 85.1% F1 score on both hate/non-hate labels. Our final submission achieved 77.0 F1 in the HASOC-2021 competition.