Battling Hateful Content in Indic Languages HASOC '21
This addresses the problem of curbing hateful content on social media for users of Indic languages, but it is incremental as it applies an existing method to a specific competition.
The paper tackled hate speech detection in multilingual and code-mixed Indic language tweets, achieving a 3rd place overall ranking out of 6 teams in the HASOC 2021 challenge.
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.