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 

机构地区：[1]Department of Computer Science and Engineering,Bangladesh University of Business and Technology,Dhaka,Bangladesh [2]School of Computer Science and Engineering,University of Aizu,Aizuwakamatsu,Japan [3]Department of Computer Science,American International University-Bangladesh,Dhaka,Bangladesh [4]Department of Computer Science and Engineering,University of Asia Pacific,Dhaka,Bangladesh

出　　处：《Computer Systems Science & Engineering》2023年第2期1775-1791,共17页计算机系统科学与工程（英文）

摘　　要：Offensive messages on social media,have recently been frequently used to harass and criticize people.In recent studies,many promising algorithms have been developed to identify offensive texts.Most algorithms analyze text in a unidirectional manner,where a bidirectional method can maximize performance results and capture semantic and contextual information in sentences.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 texts.In 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 others.This paper explores a variety of ways to deal with multilin-gualism,including collaborative multilingual and translation-based approaches.Then,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.

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

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]