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BERT-BU12 Hate Speech Detection Using Bidirectional Encoder-Decoder
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Author(s): Shailja Gupta (Manav Rachna University, India), Manpreet Kaur (Manav Rachna University, India)and Sachin Lakra (Manav Rachna University, India)
Copyright: 2022
Volume: 11
Issue: 2
Pages: 16
Source title:
International Journal of System Dynamics Applications (IJSDA)
Editor(s)-in-Chief: Ahmad Taher Azar (College of Computer & Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia & Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt)
DOI: 10.4018/IJSDA.20220701.oa4
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Abstract
In the recent times transfer learning models have known to exhibited good results in the area of text classification for question-answering, summarization, next word prediction but these learning models have not been extensively used for the problem of hate speech detection yet. We anticipate that these networks may give better results in another task of text classification i.e. hate speech detection. This paper introduces a novel method of hate speech detection based on the concept of attention networks using the BERT attention model. We have conducted exhaustive experiments and evaluation over publicly available datasets using various evaluation metrics (precision, recall and F1 score). We show that our model outperforms all the state-of-the-art methods by almost 4%. We have also discussed in detail the technical challenges faced during the implementation of the proposed model.
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