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Cross-lingual Hate Speech Detection using Transformer Models

2021-11-01 14:42:50
Teodor Tiţa, Arkaitz Zubiaga
arXiv_CL
         
Abstract

Hate speech detection within a cross-lingual setting represents a paramount area of interest for all medium and large-scale online platforms. Failing to properly address this issue on a global scale has already led over time to morally questionable real-life events, human deaths, and the perpetuation of hate itself. This paper illustrates the capabilities of fine-tuned altered multi-lingual Transformer models (mBERT, XLM-RoBERTa) regarding this crucial social data science task with cross-lingual training from English to French, vice-versa and each language on its own, including sections about iterative improvement and comparative error analysis.

Abstract (translated)

URL

https://arxiv.org/abs/2111.00981

PDF

https://arxiv.org/pdf/2111.00981.pdf

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