Overview of the HASOC track at FIRE 2020: Hate Speech and Offensive Content Identification in Indo-European Languages

Online social media platforms provide convenience for people to communicate, but the harm caused by online hate speech and offensive language accompanying them is also significant.

As mentioned in the third paragraph of the Introduction(§1), in this paper, we use two pre-trained language models based on the Transformer architecture: BERT and ALBERT to complete

In this paper, we, team MUM, propose an ensemble of Machine Learning (ML) algorithms for hate speech and offensive content identification in Indo-European languages namely,

In this paper, we presented the proposed models on the task 1 of the shared task on hate speech and offensive content identification in English, German and Hindi languages. We used

[8] used hybrid of different machine learning, and neural network based models for hate, offensive, and profane identification on multi-lingual comments of English, Hindi, and

Baruah, A., Barbhuiya, F., Dey, K.: IIITG-ADBU at HASOC 2019: Automated Hate Speech and Offensive Content Detection in English and Code-Mixed Hindi Text.. In: Proceedings of the

We describe our team 3Idiots’s approach for participating in the 2019 shared task on hate speech and offensive content (HASOC) identification in Indo-European languages.. Our

– Sub-Task 2: This sub-task is a multi class classification problem to fur- ther classify whether the document or post contains hate speech, offensive content or profane words