Deep-BERT:Transfer Learning for Classifying Multilingual Offensive Texts on Social Media

作     者：Md.Anwar Hussen Wadud M.F.Mridha Jungpil Shin Kamruddin Nur Aloke Kumar Saha 

作者机构：Department of Computer Science and EngineeringBangladesh University of Business and TechnologyDhakaBangladesh School of Computer Science and EngineeringUniversity of AizuAizuwakamatsuJapan Department of Computer ScienceAmerican International University-BangladeshDhakaBangladesh Department of Computer Science and EngineeringUniversity of Asia PacificDhakaBangladesh 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2023年第44卷第2期

页      面：1775-1791页

核心收录：

学科分类：0502[文学-外国语言文学] 050201[文学-英语语言文学] 05[文学] 

主　　题：Offensive text classification deep convolutional neural network(DCNN) bidirectional encoder representations from transformers(BERT) natural language processing(NLP) 

摘      要：Offensive messages on social media,have recently been frequently used to harass and criticize *** recent studies,many promising algorithms have been developed to identify offensive *** algorithms analyze text in a unidirectional manner,where a bidirectional method can maximize performance results and capture semantic and contextual information in *** addition,there are many separate models for identifying offensive texts based on monolin-gual and multilingual,but there are a few models that can detect both monolingual and multilingual-based offensive *** this study,a detection system has been developed for both monolingual and multilingual offensive texts by combining deep convolutional neural network and bidirectional encoder representations from transformers(Deep-BERT)to identify offensive posts on social media that are used to harass *** paper explores a variety of ways to deal with multilin-gualism,including collaborative multilingual and translation-based ***,the Deep-BERT is tested on the Bengali and English datasets,including the different bidirectional encoder representations from transformers(BERT)pre-trained word-embedding techniques,and found that the proposed Deep-BERT’s efficacy outperformed all existing offensive text classification algorithms reaching an accuracy of 91.83%.The proposed model is a state-of-the-art model that can classify both monolingual-based and multilingual-based offensive texts.