To BAN or not to BAN

Bayesian attention networks for reliable hate speech detection

Kristian Miok (Author), Blaž Škrlj (Author), Daniela Zaharie (Author), Marko Robnik Šikonja (Author)

Abstract

Hate speech is an important problem in the management of user-generated content. To remove offensive content or ban misbehaving users, content moderators need reliable hate speech detectors. Recently, deep neural networks based on the transformer architecture, such as the (multilingual) BERT model, have achieved superior performance in many natural language classification tasks, including hate speech detection. So far, these methods have not been able to quantify their output in terms of reliability. We propose a Bayesian method using Monte Carlo dropout within the attention layers of the transformer models to provide well-calibrated reliability estimates. We evaluate and visualize the results of the proposed approach on hate speech detection problems in several languages. Additionally, we test whether affective dimensions can enhance the information extracted by the BERT model in hate speech classification. Our experiments show that Monte Carlo dropout provides a viable mechanism for reliability estimation in transformer networks. Used within the BERT model, it offers state-of-the-art classification performance and can detect less trusted predictions.

Keywords

obdelava naravnega jezika;strojno učenje;nevronske mreže transformer;Bayesovske nevronske mreže;modeli BERT;natural language processing;machine learning;transformer neural networks;Bayesian neural networks;BERT models;

Data

Language:	English
Year of publishing:	2022
Typology:	1.01 - Original Scientific Article
Organization:	UL FRI - Faculty of Computer and Information Science
UDC:	004.85:81'322.2
COBISS:	80879363
ISSN:	1866-9956
Views:	19
Downloads:	4
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Slovenian
Secondary keywords:	obdelava naravnega jezika;strojno učenje;nevronske mreže transformer;bayesovske nevronske mreže;modeli BERT;
Type (COBISS):	Article
Pages:	str. 353-371
Volume:	ǂVol. ǂ14
Issue:	ǂiss. ǂ1
Chronology:	Jan. 2022
DOI:	10.1007/s12559-021-09826-9
ID:	18264590