@inproceedings{korre-etal-2024-challenges,
    title = "The Challenges of Creating a Parallel Multilingual Hate Speech Corpus: An Exploration",
    author = "Korre, Katerina  and
      Muti, Arianna  and
      Barr{\'o}n-Cede{\~n}o, Alberto",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1376",
    pages = "15842--15853",
    abstract = "Hate speech is infamously one of the most demanding topics in Natural Language Processing, as its multifacetedness is accompanied by a handful of challenges, such as multilinguality and cross-linguality. Hate speech has a subjective aspect that intensifies when referring to different cultures and different languages. In this respect, we design a pipeline that will help us explore the possibility of the creation of a parallel multilingual hate speech dataset, using machine translation. In this paper, we evaluate how/whether this is feasible by assessing the quality of the translations, calculating the toxicity levels of original and target texts, and calculating correlations between the newly obtained scores. Finally, we perform a qualitative analysis to gain further semantic and grammatical insights. With this pipeline we aim at exploring ways of filtering hate speech texts in order to parallelize sentences in multiple languages, examining the challenges of the task.",
}

Hate speech is infamously one of the most demanding topics in Natural Language Processing, as its multifacetedness is accompanied by a handful of challenges, such as multilinguality and cross-linguality. Hate speech has a subjective aspect that intensifies when referring to different cultures and different languages. In this respect, we design a pipeline that will help us explore the possibility of the creation of a parallel multilingual hate speech dataset, using machine translation. In this paper, we evaluate how/whether this is feasible by assessing the quality of the translations, calculating the toxicity levels of original and target texts, and calculating correlations between the newly obtained scores. Finally, we perform a qualitative analysis to gain further semantic and grammatical insights. With this pipeline we aim at exploring ways of filtering hate speech texts in order to parallelize sentences in multiple languages, examining the challenges of the task.
Korre, Katerina Muti, Arianna Barr{\'o}n-Cede{\~n}o, Alberto
The Challenges of Creating a Parallel Multilingual Hate Speech Corpus: An Exploration