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Applying Transfer Learning using BERT-Based Models for Hate Speech Detection

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dc.contributor.author Sharma, Yashvardhan
dc.contributor.author Chauhan, Gajendra Singh
dc.date.accessioned 2024-11-14T10:04:50Z
dc.date.available 2024-11-14T10:04:50Z
dc.date.issued 2021
dc.identifier.uri https://ceur-ws.org/Vol-3159/T1-20.pdf
dc.identifier.uri http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/16379
dc.description.abstract Hateful and Offensive speech is rising along with social media. This issue has motivated researchers to devise novel approaches which perform better than the traditional algorithms. This paper presents the methods adopted by the BITS Pilani team for Subtask 1A of the Hate Speech and Offensive Content Identification in English and Indo-Aryan Language task proposed by the Forum of Information Retrieval Evaluation in 2021. We have used data augmentation to make the models generalize better. We have experimented with different feature extraction techniques along with machine learning algorithms. But, fine-tuning the pre-trained BERT-based models using transfer learning gave us the best results for all the given languages on the test set. We got the highest Macro-F1 of 0.7993 for the English Language, 0.7612 for the Hindi Language, and 0.8306 for the Marathi Language using the pre-trained BERT-based models. en_US
dc.language.iso en en_US
dc.publisher CEUR-WS en_US
dc.subject Computer Science en_US
dc.subject Offensive language detection en_US
dc.subject Hate Speech en_US
dc.subject Label Classification en_US
dc.subject BERT-Variants en_US
dc.subject HASOC en_US
dc.title Applying Transfer Learning using BERT-Based Models for Hate Speech Detection en_US
dc.type Article en_US


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