跨语言攻击性语言检测：基于BERT的社交媒体孟加拉语、阿萨姆语与博多语对话仇恨内容分析
Cross-Linguistic Offensive Language Detection: BERT-Based Analysis of Bengali, Assamese, & Bodo Conversational Hateful Content from Social Media
AI总结：
针对社交媒体多语言仇恨言论检测问题，研究基于多种BERT模型开展HASOC-2023任务4的孟加拉语、阿萨姆语、博多语仇恨内容检测，发现XML-Roberta-large多数场景优于单语模型，团队在对应赛道取得第三、第五名的成绩。
摘要：
当今时代，社交媒体是首屈一指的交流平台，为人们提供表达猜想、学术观点与思考的渠道。遗憾的是，这种自由常伴随弊端，它助长了仇恨言论与攻击性内容的广泛传播，对世界造成有害影响，因此识别并清除社交媒体中的此类攻击性内容至关重要。本文深入探究HASOC-2023攻击性语言识别任务的综合结果与关键发现，核心重点是精准检测孟加拉语、阿萨姆语、博多语语言域内的仇恨言论，这构成了“消灭仇恨（Annihilate Hates）”任务4的框架。本研究使用了多种BERT模型，包括XML-Roberta、L3-cube、IndicBERT、BenglaBERT和BanglaHateBERT。研究结果前景良好，显示XML-Roberta-large在多数情况下表现优于单语言模型。我们的团队“TeamBD”在任务4的阿萨姆语赛道获得第3名，孟加拉语赛道获得第5名。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
