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.
翻译:由于社交媒体的激增,仇恨和有毒内容已成为当今世界的一个重大关切问题。仇恨言论和有害内容的增加促使研究人员作出大量努力,朝着挑战性的方向识别仇恨内容。在这项任务中,我们提出了对仇恨言论和冒犯性内容进行自动分类的办法。我们使用了从FIRE 2019年和2020年获得的数据集,我们利用转移学习模式进行了实验。我们注意到,经过培训的BERT模式和多语言-BERT模式取得了最佳效果。该守则公布在https://github.com/suman101112/hasoc-fire-2020上。