“…On the other hand, cross-lingual language models like XLM were also widely employed, where we found implementation of XLM-RoBERTa (XLM-R) ( Roy et al, 2021a ; Bhatia et al, 2021 ; Wang et al, 2020 ; De la Peña Sarracén & Rosso, 2022 ; Zia et al, 2022 ; Tita & Zubiaga, 2021 ; Ranasinghe & Zampieri, 2021b ; Dadu & Pant, 2020 ; Eronen et al, 2022 ; Ranasinghe & Zampieri, 2021a , 2020 ; Mozafari, Farahbakhsh & Crespi, 2022 ; Barbieri, Espinosa Anke & Camacho-Collados, 2022 ; Awal et al, 2024 ; Stappen, Brunn & Schuller, 2020 ), and both XLM-R and XLM-T ( Montariol, Riabi & Seddah, 2022 , Riabi, Montariol & Seddah, 2022 ). These approaches have all been shown to improve performance on tasks involving multilingual/cross-lingual hate speech detection because they are more likely able to capture semantic and syntactic features across languages thanks to their pre-training on multilingual large volumes of texts.…”