XRBi-GAC: A hybrid deep learning framework for multilingual toxicity detection

Author:

Singh Nitin Kumar1,Singh Pardeep1,Das Prativa2,Chand Satish1

Affiliation:

1. School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, India

2. ITER, Siksha ’O’ Anusandhan, Bhubaneswar, Odisha

Abstract

Social media platforms allow people across the globe to share their thoughts and opinions and conveniently communicate with each other. Apart from various advantages of social media, it is also misused by a set of users for hate-mongering with toxic and offensive comments. The majority of the earlier proposed toxicity detection methods are primarily focused on the English language, but there is a lack of research on low-resource languages and multilingual text data. We propose an XRBi-GAC framework comprising XLM-RoBERTa, Bi-GRU with self-attention and capsule networks for multilingual toxic text detection. A loss function is also presented, which fuses the binary cross-entropy loss and focal loss to address the class imbalance problem. We evaluated the proposed framework on two datasets, namely, the Jigsaw Multilingual Toxic Comment dataset and HASOC 2019 dataset and achieved F1-score of 0.865 and 0.829, respectively. The results of the experiments show that the proposed framework has outperformed the state-of-the-art multilingual models XLM-RoBERTa and mBERT on both datasets, which shows the versatility and robustness of the proposed XRBi-GAC framework.

Publisher

IOS Press

Subject

Artificial Intelligence,General Engineering,Statistics and Probability

Reference9 articles.

1. Sentiment analysis and classification of indian farmers’ protest using twitter data;Neogi;International Journal of Information Management DataInsights,2021

2. Vaswani A. , Shazeer N. , Parmar N. , Uszkoreit J. , Jones L. , Gomez A.N. , Kaiser Ł. and Polosukhin I. , Attention is all you need, Advances in Neural Information Processing systems 30(2017).

3. “A study of multilingual toxic textdetection approaches under imbalanced sample distribution,”;Song;Information,2021

4. “Toxicity detection in onlinegeorgian discussions,”;Lashkarashvili;International Journal of InformationManagement Data Insights,2022

5. Learningrepresentations by back-propagating errors;Rumelhart;Nature,1986

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3