Hate Speech Detection with Machine-Translated Data: The Role of Annotation Scheme, Class Imbalance and Undersampling
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3 septembre 2021
- doi: 10.4000/books.aaccademia.8345
- eisbn: 9791280136336
https://www.openedition.org/12554 , info:eu-repo/semantics/openAccess
Mots-clés
Computational Linguistics Fine-grained sentiment analysis Distributional Semantics Quantitative Linguistic Investigations Gender Bias Depression from Social Media Online Hate Speech Automatic Sarcasm Detection TrAVaSI AriEmozione AEREST COVID-19 Linguistic Ostracism in Social Networks Multilingual NLU E3C Project DistilBERT Twitter during Pandemic LAN000000 Linguistics CBXSujets proches
Skills training Speech, Hate Group libel Group defamation Defamation against groups Racist speechCiter ce document
Camilla Casula et al., « Hate Speech Detection with Machine-Translated Data: The Role of Annotation Scheme, Class Imbalance and Undersampling », Accademia University Press, ID : 10.4000/books.aaccademia.8345
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Résumé
While using machine-translated data for supervised training can alleviate data sparseness problems when dealing with less-resourced languages, it is important that the source data are not only correctly translated, but also follow the same annotation scheme and possibly class balance as the smaller dataset in the target language. We therefore present an evaluation of hate speech detection in Italian using machine-translated data from English and comparing three settings, in order to understand the impact of training size, class distribution and annotation scheme.