Transformers and Ensemble methods: A solution for Hate Speech Detection in Arabic languages
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Abstract (arXiv)
This paper describes our participation in the shared task of hate speech detection, which is one of the subtasks of the CERIST NLP Challenge 2022. Our experiments evaluate the performance of six transformer models and their combination using 2 ensemble approaches. The best results on the training set, in a five-fold cross validation scenario, were obtained by using the ensemble approach based on the majority vote. The evaluation of this approach on the test set resulted in an F1-score of 0.60 and an Accuracy of 0.86.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 12 of its 26 references that are in the map. First posted on arXiv 17 Mar 2023.
Builds on
12 of 26 references.
References · 12
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Citing papers per year: 2023: 2.
2 papers.