20.500.12556/RUL-144813 To BAN or not to BAN Bayesian attention networks for reliable hate speech detection 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. natural language processing machine learning transformer neural networks Bayesian neural networks BERT models prediction uncertainty reliability estimation Monte Carlo dropout Bayesian BERT sentic computing model calibration obdelava naravnega jezika strojno učenje nevronske mreže transformer bayesovske nevronske mreže modeli BERT true false true Angleški jezik Slovenski jezik Članek v reviji 2023-03-14 13:44:36 2023-03-14 13:44:37 2023-03-15 03:30:56 0000-00-00 00:00:00 2022 0 0 Str. 353-371 iss. 1 Vol. 14 Jan. 2022 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.85:81'322.2 1866-9956 10.1007/s12559-021-09826-9 80879363 s12559-021-09826-9.pdf s12559-021-09826-9.pdf 1 10D28EFDE9A05A29C78D6640B6BADA48 98a3f3a30ba17cdb56fe68ef01d668d45f875d32a47ce3fb2e15f9a3a6a78991 7acc8821-c266-11ed-a314-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=167350 https://link.springer.com/article/10.1007/s12559-021-09826-9 0 31ef2e3c-c266-11ed-a314-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=167349 Fakulteta za računalništvo in informatiko 0 0 0