Cross-Linguistic Offensive Language Detection: BERT-Based Analysis of
  Bengali, Assamese, & Bodo Conversational Hateful Content from Social Media
Jhuma Kabir Mim Mourad Oussalah Akash Singhal
In today's age, social media reigns as the paramount communication platform,
providing individuals with the avenue to express their conjectures,
intellectual propositions, and reflections. Unfortunately, this freedom often
comes with a downside as it facilitates the widespread proliferation of hate
speech and offensive content, leaving a deleterious impact on our world. Thus,
it becomes essential to discern and eradicate such offensive material from the
realm of social media. This article delves into the comprehensive results and
key revelations from the HASOC-2023 offensive language identification result.
The primary emphasis is placed on the meticulous detection of hate speech
within the linguistic domains of Bengali, Assamese, and Bodo, forming the
framework for Task 4: Annihilate Hates. In this work, we used BERT models,
including XML-Roberta, L3-cube, IndicBERT, BenglaBERT, and BanglaHateBERT. The
research outcomes were promising and showed that XML-Roberta-lagre performed
better than monolingual models in most cases. Our team 'TeamBD' achieved rank
3rd for Task 4 - Assamese, & 5th for Bengali.