The extensive rise in consumption of online social media (OSMs) by a large number of people poses a critical problem of curbing the spread of hateful content on these platforms. With the growing usage of OSMs in multiple languages, the task of detecting and characterizing hate becomes more complex. The subtle variations of code-mixed texts along with switching scripts only add to the complexity. This paper presents a solution for the HASOC 2021 Multilingual Twitter Hate-Speech Detection challenge by team PreCog IIIT Hyderabad. We adopt a multilingual transformer based approach and describe our architecture for all 6 subtasks as part of the challenge. Out of the 6 teams that participated in all the subtasks, our submissions rank 3rd overall.
@article{arxiv.2110.12780,
title = {Battling Hateful Content in Indic Languages HASOC '21},
author = {Aditya Kadam and Anmol Goel and Jivitesh Jain and Jushaan Singh Kalra and Mallika Subramanian and Manvith Reddy and Prashant Kodali and T. H. Arjun and Manish Shrivastava and Ponnurangam Kumaraguru},
journal= {arXiv preprint arXiv:2110.12780},
year = {2021}
}
Comments
12 pages, 6 figures, 2 tables, Accepted at FIRE 2021, CEUR Workshop Proceedings (http://fire.irsi.res.in/fire/2021/home)