Multilingual Hate Speech and Offensive Content Detection using Modified Cross-entropy Loss

• URL: http://arxiv.org/abs/2202.02635v1
• Date: Sat, 5 Feb 2022 20:31:40 GMT
• Title: Multilingual Hate Speech and Offensive Content Detection using Modified Cross-entropy Loss
• Authors: Arka Mitra, Priyanshu Sankhala
• Abstract summary: Large language models are trained on a lot of data and they also make use of contextual embeddings. The data is also quite unbalanced; so we used a modified cross-entropy loss to tackle the issue. Our team (HNLP) achieved the macro F1-scores of 0.808, 0.639 in English Subtask A and English Subtask B respectively.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The number of increased social media users has led to a lot of people misusing these platforms to spread offensive content and use hate speech. Manual tracking the vast amount of posts is impractical so it is necessary to devise automated methods to identify them quickly. Large language models are trained on a lot of data and they also make use of contextual embeddings. We fine-tune the large language models to help in our task. The data is also quite unbalanced; so we used a modified cross-entropy loss to tackle the issue. We observed that using a model which is fine-tuned in hindi corpora performs better. Our team (HNLP) achieved the macro F1-scores of 0.808, 0.639 in English Subtask A and English Subtask B respectively. For Hindi Subtask A, Hindi Subtask B our team achieved macro F1-scores of 0.737, 0.443 respectively in HASOC 2021.

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