Abstract
The identification of offensive language on social media has been a widely studied problem in recent years owing to the volume of data generated by these platforms and its consequences. In this paper, we present the results of our experiments on the OLID dataset from the OffensEval shared from SemEval 2019. We use both traditional machine learning methods and state of the art transformer models like BERT to set a baseline for our experiments. Following this, we propose the use of fine-tuning Distilled Bert using both OLID and an additional hate speech and offensive language dataset. Then, we evaluate our model on the test set, yielding a macro f1 score of 78.8.
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Singh, A., Ray, R. (2021). Identifying Offensive Content in Social Media Posts. In: Chakraborty, T., Shu, K., Bernard, H.R., Liu, H., Akhtar, M.S. (eds) Combating Online Hostile Posts in Regional Languages during Emergency Situation. CONSTRAINT 2021. Communications in Computer and Information Science, vol 1402. Springer, Cham. https://doi.org/10.1007/978-3-030-73696-5_1
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