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.

本文研究了在社交媒体领域中波斯尼亚语、塔吉克语、哈萨克语和蒙古语的仇恨言论检测的详细结果和关键发现。通过使用BERT模型，特别是XML-Roberta-lagre，在大多数情况下，我们的成果表明其性能优于单语模型。我们的团队在任务4-塔吉克语中获得第3名，在波斯尼亚语中获得第5名。

跨语言攻击性语言检测：来自社交媒体的孟加拉语、阿萨姆语和博多语对话仇恨内容的基于BERT的分析