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We recognize the importance of protecting individuals from the adverse effects of hate speech and the need to balance this with upholding free speech. Content moderation is one application where our method could help censor hate speech on social media platforms such as Twitter, Facebook, Reddit, etc. However, one ethical concern is our system\u2019s false positives, i.e., if the system incorrectly flags a user\u2019s text as hate speech, it may censor legitimate free speech. Therefore, we discourage incorporating our methodology in a purely automated manner for any real-world content moderation system until and unless a human annotator works alongside the system to determine the final decision. <b>Use of Hate Speech Datasets<\/b>. In our work, we incorporated publicly available well-established datasets. We have correctly cited the corresponding dataset papers and followed the necessary steps in utilizing those datasets in our work. We understand that the hate speech examples used in the paper are potentially harmful content that could be used for malicious activities. However, our work aims to help better investigate and help mitigate the harms of online hate. Therefore, we have assessed that the benefits of using these real-world examples to explain our work better outweigh the potential risks. <b>Fairness and Bias in Detection.<\/b> Our work values the principles of fairness and impartiality. 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