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.
@article{arxiv.2111.00981,
title = {Cross-lingual Hate Speech Detection using Transformer Models},
author = {Teodor Tiţa and Arkaitz Zubiaga},
journal= {arXiv preprint arXiv:2111.00981},
year = {2021}
}