Papers › HASOCOne@FIRE-HASOC2020: Using BERT and Multilingual BERT models for Hate Speech Detection

HASOCOne@FIRE-HASOC2020: Using BERT and Multilingual BERT models for Hate Speech Detection

22 Jan 2021arXiv:2101.09007archive 2025-07-28

Suman Dowlagar, Radhika Mamidi

Hateful and Toxic content has become a significant concern in today's world due to an exponential rise in social media. The increase in hate speech and harmful content motivated researchers to dedicate substantial efforts to the challenging direction of hateful content identification. In this task, we propose an approach to automatically classify hate speech and offensive content. We have used the datasets obtained from FIRE 2019 and 2020 shared tasks. We perform experiments by taking advantage of transfer learning models. We observed that the pre-trained BERT model and the multilingual-BERT model gave the best results. The code is made publically available at https://github.com/suman101112/hasoc-fire-2020.

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Hate Speech Detection Transfer Learning

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Adam Attention Attention Dropout BERT Dense Connections Dropout Layer Normalization Linear Layer Linear Warmup With Linear Decay Multi-Head Attention Residual Connection Softmax Weight Decay WordPiece

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